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Regulatory Impact Analysis for the Proposed National
Emission Standards for Hazardous Air Pollutants:
Coal- and Oil-Fired Electric Utility Steam Generating
Units Review of the Residual Risk and Technology
Review
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
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EPA-452/R-23-002
April 2023
Regulatory Impact Analysis for the Proposed National Emission Standards for Hazardous Air
Pollutants: Coal- and Oil-Fired Electric Utility Steam Generating Units Review of the Residual
Risk and Technology Review
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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CONTACT INFORMATION
This document has been prepared by staff from the Office of Air and Radiation, U.S.
Environmental Protection Agency. Questions related to this document should be addressed to the
Air Economics Group in the Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Office of Air and Radiation, Research Triangle Park, North Carolina 27711
(email: OAQPSeconomics@epa.gov).
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TABLE OF CONTENTS
Table of Contents i
List of Tables iv
List of Figures vii
Executive Summary ES-1
ES.l Introduction ES-1
ES.2 Regulatory Requirements ES-2
ES.3 Baseline and Analysis Years ES-4
ES.4 Emissions Impacts ES-5
ES.5 Compliance Costs ES-6
ES.6 Benefits ES-8
ES.6. lHealth Benefits ES-8
ES.6.2 Climate Benefits ES-9
ES.6.3 Additional Unqualified Benefits ES-10
ES.6.4 Total Health and Climate Benefits ES-10
ES.7 Environmental Justice Impacts ES-11
ES.8 Comparison of Benefits and Costs ES-14
ES.9 References ES-16
1 Introduction and Background 1-1
1.1 Introduction 1-1
1.2 Legal and Economic Basis for Rulemaking 1-2
1.2.1 Statutory Requirement 1-2
1.2.2 Regulated Pollutants 1-2
1.2.3 The Need for Air Emissions Regulation 1-3
1.3 Overview of Regulatory Impact Analysis 1-4
1.3.1 Regulatory Options 1-4
1.3.2 Baseline and Analysis Years 1-7
1.4 Organization of the Regulatory Impact Analysis 1-7
2 Industry Profile 2-1
2.1 Background 2-1
2.2 Power Sector Overview 2-1
2.2.1 Generation 2-1
2.2.2 Transmission 2-9
2.2.3 Distribution 2-10
2.3 Sales, Expenses, and Prices 2-11
2.3.1 Electricity Prices 2-11
2.3.2 Prices of Fossil Fuel Used for Generating Electricity 2-13
2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2010 to 2021 2-14
3 Costs, Emissions, and Energy Impacts 3-1
3.1 Introduction 3-1
3.2 EPA'S Post-IRA 2022 REFERENCE CASE 3-1
3.3 Baseline 3-4
3.4 Regulatory Options Analyzed 3-5
3.5 Power Sector Impacts 3-7
3.5.1 Emissions 3-7
3.5.2 Compliance Costs 3-8
3.5.3 Projected Compliance Actions for Emissions Reductions 3-13
3.5.4 Generating Capacity 3-14
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3.5.5 Generation Mix 3-18
3.5.6 Coal and Natural Gas Use for the Electric Power Sector 3-19
3.5.7 Fuel Price, Market, and Infrastructure 3-21
3.5.8 Retail Electricity Prices 3-23
3.6 Limitations of Analysis and Key Areas of Uncertainty 3-27
3.7 References 3-29
4 Benefits Analysis 4-1
4.1 Introduction 4-1
4.2 Hazardous Air Pollutant Benefits 4-3
4.2.1 Mercury 4-3
4.2.2 Metal HAP 4-6
4.2.3 Additional HAP B enefits 4-7
4.3 Criteria Pollutant Benefits 4-8
4.3.1 Air Quality Modeling Methodology 4-9
4.3.2 Selecting Air Pollution Health Endpoints to Quantify 4-10
4.3.3 Calculating Counts of Air Pollution Effects Using the Health Impact Function 4-13
4.3.4 Calculating the Economic Valuation of Health Impacts 4-15
4.3.5 Benefits Analysis Data Inputs 4-15
4.3.6 Quantifying Cases of Ozone-Attributable Premature Death 4-21
4.3.7 Quantifying Cases of PM2 s-Attributable Premature Death 4-23
4.3.8 Characterizing Uncertainty in the Estimated Benefits 4-23
4.3.9 Estimated Number and Economic Value of Health Benefits 4-26
4.3.10 Additional Unqualified Criteria Pollutant Benefits 4-34
4.4 Climate Pollutant Benefits 4-41
4.5 Water Quality and Availability Benefits 4-55
4.6 Total Benefits 4-59
4.7 References 4-64
5 Economic Impacts 5-1
5.1 Overview 5-1
5.2 Small Entity Analysis 5-1
5.2.1 Methodology 5-2
5.2.2 Results 5-7
5.2.3 Conclusion 5-8
5.3 Labor Impacts 5-9
5.3.1 Overview of Methodology 5-11
5.3.2 Overview of Power Sector Employment 5-12
5.3.3 Projected Sectoral Employment Changes due to the Proposed Rule 5-13
5.3.4 Conclusions 5-14
5.4 References 5-15
6 Environmental Justice Impacts 6-1
6.1 Introduction 6-1
6.2 Analyzing EJ Impacts in This Proposal 6-3
6.3 Qualitative Assessment of HAP Impacts 6-4
6.4 Demographic Proximity Analyses of Existing Facilities 6-6
6.5 EJ PM2.5 and Ozone Exposure Impacts 6-9
6.5.1 Populations Predicted to Experience PM2 5 and Ozone Air Quality Changes 6-12
6.5.2 PM2 5 EJ Exposure Analysis 6-13
6.5.3 Ozone EJ Exposure Analysis 6-18
6.6 Qualitative Assessment of Climate Impacts 6-25
6.7 Summary 6-28
7 Comparison of Benefits and Costs 7-1
11
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7.1 Introduction 7-1
7.2 Methods 7-1
7.3 Results 7-2
Appendix A: Air Quality Modeling A-l
A. 1 Introduction A-l
A.2 Air Quality Modeling Simulations A-l
A. 3 Applying Modeling Outputs to Create Spatial Fields A-8
A. 4 Scaling Factors Applied to Source Apportionment Tags A-15
A. 5 Air Quality Surface Results A-22
A. 6 Uncertainties and Limitations of the Air Quality Methodology A-26
A. 7 References A-27
iii
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LIST OF TABLES
Table
ES-1
Table
ES-2
Table
ES-3
Table
ES-4
Table
ES-5
Table
1-1
Table
2-1
Table
2-2
Table
2-3
Table
2-4
Table
3-1
Table
3-2
Table
3-3
Table
3-4
Table
3-5
Table
3-6
Table
3-7
Table
3-8
Table
3-9
Table
3-10
Table
3-11
Table
3-12
Table
3-13
Table
3-14
Table
3-15
Table
3-16
Table
3-17
Table
3-18
Table
3-19
Table
4-1
Table
4-2
Summary of Proposed Regulatory Options Examined in this RIA ES-4
Projected EGU Emissions and Emissions Changes for the Baseline and the Regulatory Control
Alternatives for 2028, 2030, and 2035 ES-6
Total National Compliance Cost Estimates for the Proposed Rule and the Less and More Stringent
Alternatives (millions of 2019 dollars, discounted to 2023) ES-7
Monetized Health Benefits and Climate Benefits for the Proposed Rule from 2028 through 2037
(millions of 2019 dollars, discounted to 2023) ES-11
Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent
Alternatives (millions of 2019 dollars, discounted to 2023) ES-15
Summary of Proposed Regulatory Options Examined in this RIA 1-7
Total Net Summer Electricity Generating Capacity by Energy Source, 2015 and 2021 2-3
Net Generation in 2015 and 2021 (Trillion kWh = TWh) 2-4
Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average Heat Rate in 2020 ... 2-7
Total U.S. Electric Power Industry Retail Sales, 2015 and 2021 (billion kWh) 2-11
Summary of Proposed Regulatory Options Examined in this RIA 3-6
PM Control Technology Modeling Assumptions 3-6
EGU Emissions and Emissions Changes for the Baseline Run and the Proposed Rule and More
Stringent Alternatives for 2028, 2030, and 2035 3-8
National Power Sector Compliance Cost Estimates (millions of 2019 dollars) for the Proposed Rule
and More Stringent Alternative for 2028, 2030, and 2035 3-9
Costs of Proposed Continuous Emissions Monitoring (PM CEMS) Requirement 3-11
Stream of Projected Compliance Costs across Proposed Rule and Less and More Stringent Regulatory
Alternatives (millions of 2019 dollars) 3-13
Affected Capacity Operational in the Baseline by PM Control Strategy for the Proposed Rule and
More Stringent Alternative in 2028 (GW) 3-14
2028, 2030, and 2035 Projected U.S. Capacity by Fuel Type for the Baseline and the Proposed Rule
and More Stringent Alternative 3-15
2028, 2030, and 2035 Projected U.S. Retirements by Fuel Type for the Baseline Run and the
Proposed Rule and More Stringent Alternative 3-16
2028, 2030, and 2035 Projected U.S. New Capacity Builds by Fuel Type for the Baseline Run and the
Proposed Rule and More Stringent Alternative 3-17
2028, 2030, and 2035 Projected U.S. Generation by Fuel Type for the Baseline Run and the Proposed
Rule and More Stringent Alternative 3-19
2028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Coal Supply Region for the Baseline
Run and the Proposed Rule and More Stringent Alternative 3-20
2028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Rank for the Baseline Run and the
Proposed Rule and More Stringent Alternative 3-21
2028, 2030, and 2035 Projected U.S. Power Sector Natural Gas Use for the Baseline Run and the
Proposed Rule and More Stringent Alternative 3-21
2028, 2030, and 2035 Projected Minemouth and Power Sector Delivered Coal Price (2019 dollars) for
the Baseline and the Proposed Rule and More Stringent Alternative 3-22
2028, 2030, and 2035 Projected Henry Hub and Power Sector Delivered Natural Gas Price (2019
dollars) for the Baseline and the Proposed Rule and More Stringent Alternative 3-22
Average Retail Electricity Price by Region for the Baseline and the Proposed Rule and More Stringent
Alternative, 2028 3-24
Average Retail Electricity Price by Region for the Baseline and the Proposed Rule and More Stringent
Alternative, 2030 3-25
Average Retail Electricity Price by Region for the Baseline and the Proposed Rule and More Stringent
Alternative, 2035 3-26
Health Effects of Ambient Ozone and PM2 5 and Climate Effects 4-12
Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Proposed
Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) 4-27
IV
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Table 4-3 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the More
Stringent Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) 4-28
Table 4-4 Estimated Avoided PM2 s-Related Premature Respiratory Mortalities and Illnesses for the Proposed
Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval) 4-29
Table 4-5 Estimated Avoided PM2 s-Related Premature Respiratory Mortalities and Illnesses for the More
Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval) 4-30
Table 4-6 Estimated Discounted Economic Value of Avoided Ozone and PM2 s-Attributable Premature
Mortality and Illness for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent
confidence interval; millions of 2019 dollars) 4-31
Table 4-7 Estimated Discounted Economic Value of Avoided Ozone and PM2 s-Attributable Premature
Mortality and Illness for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent
confidence interval; millions of 2019 dollars) 4-32
Table 4-8 Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified
as Sum of Long-Term Ozone Mortality and Long-Term PM2 5 Mortality (discounted at 3 percent;
millions of 2019 dollars) 4-33
Table 4-9 Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified
as Sum of Long-Term Ozone Mortality and Long-Term PM2 5 Mortality (discounted at 7 percent;
millions of 2019 dollars) 4-33
Table 4-10 Additional Unqualified Benefit Categories 4-35
Table 4-11 Interim Social Cost of Carbon Values, 2025-2040 (2019 dollars per Metric Tonne C02) 4-49
Table 4-12 Estimated Climate Benefits from Changes in C02 Emissions for 2028, 2030, and 2035 (millions of
2019 dollars) 4-54
Table 4-13 Stream of Projected Climate Benefits under Proposed Rule from 2028 through 2037 (millions of 2019
dollars) 4-54
Table 4-14 Stream of Projected Climate Benefits under More Stringent Regulatory Option from 2028 through
2037 (millions of 2019 dollars) 4-55
Table 4-15 Combined PM2 5 and 03-related Health Benefits and Climate Benefits for the Proposed Requirements
and More Stringent Alternative for 2028 (millions of 2019 dollars) 4-60
Table 4-16 Combined PM2 5 and 03-related Health Benefits and Climate Benefits for the Proposed Requirements
and More Stringent Alternative for 2030 (millions of 2019 dollars) 4-60
Table 4-17 Combined PM2 5 and 03-related Health Benefits and Climate Benefits for the Proposed Requirements
and More Stringent Alternative for 2035 (millions of 2019 dollars) 4-61
Table 4-18 Stream of Combined PM2 5 and 03-related Health Benefits and Climate Benefits for the Proposed
Rule from 2028 through 2037 (millions of 2019 dollars) 4-62
Table 4-19 Stream of Combined PM2 5 and 03-related Health Benefits and Climate Benefits for the More
Stringent Regulatory Option from 2028 through 2037 (millions of 2019 dollars) 4-63
Table 5-1 SBA Size Standards by NAICS Code 5-4
Table 5-2 Projected Impacts of Proposal on Small Entities in 2028 5-8
Table 5-3 Changes in Labor Utilization: Construction-Related (Number of Job-Years of Employment in a Single
Year) 5-13
Table 5-4 Changes in Labor Utilization: Recurring Non-Construction (Number of Job-Years of Employment in
a Single Year) 5-14
Table 6-1 Proximity Demographic Assessment Results Within 10 km of Coal-Fired Units Greater than 25 MW
Without Retirement or Gas Conversion Plans Before 2029 Affected by this Proposed Rulemaking. 6-9
Table 6-2 Demographic Populations Included in the PM2 5 and Ozone EJ Exposure Analyses 6-12
Table 7-1 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent
Alternatives for 2028 for the U.S. (millions of 2019 dollars) 7-3
Table 7-2 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent
Alternatives for 2030 for the U.S. (millions of 2019 dollars) 7-4
Table 7-3 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent
Alternatives for 2035 for the U.S. (millions of 2019 dollars) 7-4
Table 7-4 Proposed Rule: Present Values and Equivalent Annualized Values of Projected Monetized
Compliance Costs, Benefits, and Net Benefits for 2028 to 2037 (millions of 2019 dollars, discounted
to 2023) 7-5
V
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Table 7-5 Less Stringent Regulatory Option: Present Values and Equivalent Annualized Values for the 2028 to
2037 Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits (millions of
2019 dollars, discounted to 2023) 7-6
Table 7-6 More Stringent Regulatory Option: Present Values and Equivalent Annualized Values for the 2028 to
2037 Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits (millions of
2019 dollars, discounted to 2023) 7-7
Table A-l 2026 Emissions Allocated to Each Modeled State-EGU Source Apportionment Tag A-4
Table A-2 Ozone Scaling Factors for EGU Tags in the Baseline, the Proposed Rule, and More Stringent
Alternative A-15
Table A-3 Nitrate Scaling Factors for EGU Tags in the Baseline, the Proposed Rule, and More Stringent
Alternative A-17
Table A-4 Sulfate Scaling Factors for EGU Tags in the Baseline, the Proposed Rule, and More Stringent
Alternative A-19
Table A-5 Primary PM2 5 Scaling Factors for EGU Tags in the Baseline, the Proposed Rule, and More Stringent
Alternative A-21
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LIST OF FIGURES
Figure 2-1 National Coal-fired Capacity (GW) by Age of EGU, 2021 2-5
Figure 2-2 Average Annual Capacity Factor by Energy Source 2-6
Figure 2-3 Cumulative Distribution in 2019 of Coal and Natural Gas Electricity Capacity and Generation, by Age
2-8
Figure 2-4 Fossil Fuel-Fired Electricity Generating Facilities, by Size 2-9
Figure 2-5 Real National Average Electricity Prices (including taxes) for Three Major End-Use Categories.. 2-12
Figure 2-6 Relative Increases in Nominal National Average Electricity Prices for Major End-Use Categories
(including taxes), With Inflation Indices 2-13
Figure 2-7 Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National Average Real
Price per MMBtu Delivered to EGU 2-14
Figure 2-8 Relative Growth of Electricity Generation, Population and Real GDP Since 2014 2-15
Figure 2-9 Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2010 2-16
Figure 3-1 Electricity Market Module Regions 3 -27
Figure 4-1 Data Inputs and Outputs for the BenMAP-CE Tool 4-16
Figure 4-2 Frequency Distribution of SC-CO2 Estimates for 2030 4-50
Figure 6-1 Number of People Residing in the Contiguous U.S., Areas Improving or Not Changing (Teal) or
Worsening (Red) in 2028, 2030, and 2035 for PM2 5 and Ozone and the National Average Magnitude
of Pollutant Concentration Changes (|ig/m3 and ppb) for the Proposed and More Stringent Regulatory
Options 6-13
Figure 6-2 Heat Map of the National Average PM2 5 Concentrations in the Baseline and Reductions in
Concentrations Due to the Proposed and More Stringent Regulatory Options Across Demographic
Groups in 2028, 2030, and 2035 (ng/m3) 6-15
Figure 6-3 Heat Map of the State Average PM2 5 Concentration Reductions (Blue) and Increases (Red) Due to the
Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and
2035 (ug/m:) 6-16
Figure 6-4 Distributions of PM25 Concentration Changes Across Populations, Future Years, and Regulatory
Options 6-18
Figure 6-5 Heat Map of the National Average Ozone Concentrations in the Baseline and Reductions in
Concentrations Due to the Proposed and More Stringent Regulatory Options Across Demographic
Groups in 2028, 2030, and 2035 (ppb) 6-21
Figure 6-6 Heat Map of the State Average Ozone Concentrations Reductions (Green) and Increases (Red) Due to
the Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and
2035 (ppb) 6-23
Figure 6-7 Distributions of Ozone Concentration Changes Across Populations, Future Years, and Regulatory
Options 6-25
Figure A-l Air Quality Modeling Domain A-3
Figure A-2 Maps of California EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone
(ppb); b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 Sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-6
Figure A-3 Maps of Texas EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate |ig/m3): c) Annual Average PM2 5, Sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-7
Figure A-4 Maps of Iowa EGU Tag contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 Sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-7
Figure A-5 Maps of Ohio EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 Sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-8
Figure A-6 Maps of ASM-03 in 2028 A-24
Figure A-7 Maps of ASM-03 in 2030 A-25
Figure A-8 Maps of ASM-03 in 2035 A-25
Figure A-9 Maps of PM2 5 in 2028 A-25
Figure A-10 Maps of PM25 in 2030 A-26
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Figure A-l 1 Maps of PM2.5 in 2035 A-26
viii
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EXECUTIVE SUMMARY
ES.l Introduction
On January 20, 2021, President Biden signed E.O. 13990, "Protecting Public Health and
the Environment and Restoring Science to Tackle the Climate Crisis" (86 FR 7037; January 25,
2021). The executive order instructs EPA, inter alia, to review the 2020 final action titled,
"National Emission Standards for Hazardous Air Pollutants: Coal- and Oil-Fired Electric Utility
Steam Generating Units-Reconsideration of Supplemental Finding and Residual Risk and
Technology Review" (85 FR 31286; May 22, 2020) (2020 Final Action) and to consider
publishing a notice of proposed rulemaking suspending, revising, or rescinding that action. The
2020 Final Action included a finding that it is not appropriate and necessary to regulate coal and
oil-fired electric utility steam generating units (EGUs) under Clean Air Act (CAA) section 112
as well as the RTR for the National Emission Standards for Hazardous Air Pollutants (NESHAP)
for Coal- and Oil-Fired EGUs, commonly referred to, including within this document, as the
Mercury and Air Toxics Standards (MATS). The results of EPA's review of the appropriate and
necessary finding were proposed on February 9, 2022 (87 FR 7624) and finalized on March 6,
2023 (88 FR 13956). This RIA presents the expected economic consequences of EPA's proposed
MATS Risk and Technology Review.
In accordance with E.O. 12866 and 13563, the guidelines of OMB Circular A-4 and
EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2014), the RIA analyzes the
benefits and costs associated with the projected emissions reductions under the proposed
requirements, a less stringent set of requirements, and a more stringent set of requirements to
inform the EPA and the public about these projected impacts. The benefits and costs of the
proposed rule and regulatory alternatives are presented for the 2028 to 2037 time period.
This proposed rule is projected to reduce emissions of mercury and non-mercury metal
HAP at a national level. Mercury emitted from U.S. EGUs can deposit to watersheds and
associated waterbodies where it can accumulate as methylmercury in fish. Methylmercury is
known to adversely impact neurological function and development and to exert some genotoxic
activity and EPA has classified methylmercury as a "possible" human carcinogen. Reductions in
methylmercury fish burden and human exposure reduces the potential for these adverse effects.
ES-1
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In addition, U.S. EGUs are a major source of non-mercury metallic HAP emissions The
proposed controls are expected to reduce human exposure to non-mercury metallic HAP.
ES.2 Regulatory Requirements
For coal-fired EGUs, the MATS rule established standards to limit emissions of mercury,
acid gas HAP, non-mercury HAP metals (e.g., nickel, lead, chromium), and organic HAP (e.g.,
formaldehyde, dioxin/furan). 77 FR 9310. Standards for hydrochloric acid (HC1) serve as a
surrogate for the acid gas HAP, with an alternate standard for sulfur dioxide (SO2) that may be
used as a surrogate for acid gas HAP for those coal-fired EGUs with flue gas desulfurization
(FGD) systems and SO2 continuous emission monitoring systems (CEMS) installed and
operational. Standards for filterable particulate matter serve as a surrogate for the non-mercury
HAP metals, with standards for total non-mercury HAP metals and individual non-mercury HAP
metals provided as alternative equivalent standards. Work practice standards limit formation and
emission of the organic HAP.
For oil-fired EGUs, the rule established standards to limit emissions of HC1 and hydrogen
fluoride (HF), total HAP metals (e.g., mercury, nickel, lead), and organic HAP (e.g.,
formaldehyde, dioxin/furan). Standards for PM serve as a surrogate for total HAP metals, with
standards for total HAP metals and individual HAP metals provided as alternative equivalent
standards. Work practice standards limit formation and emission of the organic HAP.
While more detail can be found in the preamble of the proposed rule and in Section 1.3.1
of this document, this RIA focuses on evaluating the benefits, costs, and other impacts of four
proposed amendments to the MATS rule, as follows:
• Tightening the Standard for Non-Mercury Metal HAP Emissions for Existing Coal-
fired EGUs: Existing coal-fired EGUs are subject to numeric emission limits for
filterable PM, a surrogate for the total non-mercury HAP metals.1 MATS currently
requires existing coal-fired EGUs to meet a filterable particulate matter emission standard
1 As described in section III of the preamble to this proposed rule, EGUs in six subcategories are subject to numeric
emission limits for specific HAP or fPM, a surrogate for the total non-mercury HAP metals. The fPM was chosen as
a surrogate in the original rulemaking because the non-mercury HAP metals are predominantly a component of PM,
and control of PM will also result in co-reduction of non-mercury HAP metals. Additionally, not all fuels emit the
same type and amount of metallic HAP, but most generally emit PM that include some amount and combination of
all the metallic HAP. Lastly, the use of fPM as a surrogate eliminates the cost of performance testing to comply with
numerous standards for individual non-mercury metal HAP (Docket ID No. EPA-HQ-OAR-2009-0234). For these
reasons, the EPA focused its review on the fPM emissions of coal-fired EGUs as a surrogate for the non-mercury
metal HAP.
ES-2
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of 0.030 pounds per million British thermal units (lb/MMBtu) of heat input. After
reviewing updated information on the current emission levels of filterable PM from
existing coal-fired EGUs and the costs of meeting a standard more stringent than 0.030
lb/MMBtu, EPA is proposing to revise the filterable PM emission standard for existing
coal-fired EGUs to 0.010 lb/MMBtu. EPA also solicits comment on requiring existing
coal-fired EGUs to meet a filterable PM standard of 0.006 lb/MMBtu.
• Mercury Emission Standard for Lignite-fired EGUs: EPA is also proposing to revise
the mercury emission standard for existing lignite-fired EGUs. Currently, lignite-fired
EGUs must meet a mercury emission standard of 4.0 pounds per trillion British thermal
units (lb/TBtu) or 4.0E-2 pounds per gigawatt hour (lb/GWh). EPA is proposing that
lignite-fired EGUs meet the same standard as existing EGUs firing other types of coal,
1.2 lb/TBtu or 1.3E-2 lb/GWh.
• Continuous Emissions Monitoring Systems: After considering updated information on
the costs for performance testing compared to the cost of PM CEMS and capabilities of
PM CEMS measurement abilities, as well as the benefits of using PM CEMS, which
include increased transparency and accelerated identification of anomalous emissions,
EPA is proposing to require that all coal-fired EGUs demonstrate compliance with the
PM emission standard by using PM CEMS. Currently EGUs have a choice of
demonstrating compliance with the non-mercury HAP metals by monitoring filterable
PM with quarterly sampling or PM CEMS.
• Startup Definitions: EPA is proposing to remove one of the two options for defining the
startup period for EGUs. The first option defines startup as either the first-ever firing of
fuel in a boiler for the purpose of producing electricity, or the firing of fuel in a boiler
after a shutdown event for any purpose. In the second option, startup is defined as the
period in which operation of an EGU is initiated for any purpose. EPA is proposing to
remove the second option, which is currently being used by fewer than 10 EGUs.
Table ES-1 summarizes how we have structured the regulatory options to be analyzed in
this RIA. The proposed regulatory option includes the proposed amendments just discussed in
this section: the proposed revision to the filterable PM standard to 0.010 lb/MMBtu, in which
PM is a surrogate for non-mercury metal HAP, the proposed revision to the mercury standard for
lignite-fired EGUs to 1.2 lb/TBtu, the proposal to require PM CEMS to demonstrate compliance,
and the removal of the startup definition number two. The more stringent regulatory option
examined in this RIA tightens the proposed revision to the filterable PM standard to 0.006
lb/MMBtu. Note EPA is soliciting comment on this more stringent filterable PM standard. The
other three proposed amendments are not changed in the more stringent regulatory option
examined in this RIA. Finally, the less stringent regulatory option examined in this RIA assumed
ES-3
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the filterable PM and mercury limits remain unchanged and examines just the proposed PM
CEMS requirement and removal of startup definition number two.
Table ES-1 Summary of Proposed Regulatory Options Examined in this RIA
Regulatory Options Examined in this RIA
Provision
Less Stringent
Proposed
More Stringent
Filterable PM
Standard
(Surrogate
Standard for Non-
Hg metal HAP)
Retain existing filterable
PM standard of 0.030
lb/MMBtu
Revised filterable PM
standard of 0.010
lb/MMBtu
Revised filterable PM
standard of 0.006
lb/MMBtu
Mercury Standard
Retain mercury standard
for lignite-fired EGUs of
4.0 lb/TBtu
Revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
Revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
Continuous
Emissions
Monitoring Systems
(PM CEMS)
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Startup definition
Remove startup definition
#2
Remove startup definition
#2
Remove startup definition
#2
ES.3 Baseline and Analysis Years
The impacts of proposed regulatory actions are evaluated relative to a modeled baseline
that represents expected behavior in the electricity sector under market and regulatory conditions
in the absence of a regulatory action. EPA frequently updates the power sector modeling baseline
to reflect the latest available electricity demand forecasts from the U.S. Energy Information
Administration (EIA) as well as expected costs and availability of new and existing generating
resources, fuels, emission control technologies, and regulatory requirements. The baseline
includes the proposed Good Neighbor Plan (GNP), the Revised Cross-State Air Pollution Rule
(CSAPR) Update, CSAPR Update, and CSAPR, as well as the Mercury and Air Toxics
Standards. The power sector baseline also includes the 2015 Effluent Limitation Guidelines
(ELG) and the 2015 Coal Combustion Residuals (CCR), and the recently finalized 2020 ELG
and CCR rules. This version of the model ("EPA's Post-IRA 2022 Reference Case") also
includes recent updates to state and federal legislation affecting the power sector, including
Public Law 117-169, 136 Stat. 1818 (August 16, 2022), commonly known as the Inflation
Reduction Act of 2022 (IRA). The modeling documentation includes a summary of all
legislation reflected in this version of the model as well as a description of how that legislation is
ES-4
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implemented in the model.2 Also, see Section 3.3 for additional detail about the power sector
baseline for this RIA.
All analysis begins in the year 2028, the compliance year for the proposed standards. In
addition, the regulatory impacts are evaluated for the specific analysis years of 2030 and 2035.
These results are used to estimate the present value (PV) and equivalent annualized value (EAV)
of the 2028 through 2037 period, discounted to 2023.
ES.4 Emissions Impacts
The emissions reductions presented in this RIA are from years 2028, 2030, and 2035 and
are based on IPM projections. Table ES-2 presents the estimated impact on power sector
emissions resulting from compliance with the evaluated regulatory control alternatives in the
contiguous U.S. As the incremental cost of requiring PM CEMS is negative and small relative to
other aspects of this proposed rulemaking, the less stringent regulatory alternative was not
modeled using IPM. The projections indicate that both the proposed rule and the more stringent
alternative result in emissions reductions in all run years, and those emission reductions follow
an expected pattern: the proposed rule, which revises the filterable PM standard to 0.010
lb/MMBtu, produces smaller emissions reductions than the more stringent alternative, which
evaluates a lower filterable PM standard to limit of 0.006 lb/MMBtu. The additional reductions
of mercury emissions in the more stringent alternative are largely attributable to the additional
projected coal steam retirements in this scenario.
2 See document titled "Documentation for EPA's Power Sector Modeling Platform v6 Using the Integrated Planning
Model Post-IRA 2022 Reference Case", which is available in the docket for this action.
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Table ES-2 Projected EGU Emissions and Emissions Changes for the Baseline and the
Regulatory Control Alternatives for 2028, 2030, and 2035 a
Total Emissions Change from Baseline
Year
Baseline
Run
Proposed
Rule
More-
Stringent
Alternative
Proposed
Rule
More-
Stringent
Alternative
2028
5,019
4,957
4,811
-62
-208
Mercury (lbs.)
2030
4,206
4,139
4,037
-67
-169
2035
3,219
3,137
3,052
-82
-168
PM2.5 (thousand
tons)
2028
2030
74.6
65.5
74.2
65.1
72
64
-0.4
-0.4
-2.6
-1.5
2035
46.6
45.8
45.3
-0.8
-1.3
2028
394
393
382
-0.9
-11.6
SO2 (thousand tons)
2030
282
282
282
-0.5
-0.3
2035
130
128
121
-1.5
-8.8
Ozone-season NOx
(thousand tons)
2028
2030
2035
195
163
104
195
163
101
188
158
99
-0.2
-0.4
-3.2
-7.2
-5.1
-5.6
Annual NOx
(thousand tons)
2028
2030
2035
457
368
214
456
367
211
439
358
205
-0.4
-0.8
-3.4
-18.1
-9.5
-8.7
2028
2.6
2.6
2.5
0.0
-0.2
HC1 (thousand tons)
2030
1.8
1.8
1.7
0.0
-0.1
2035
0.9
0.9
0.8
0.0
-0.1
CO2 (million
metric tons)
2028
2030
2035
1222
972
608
1222
971
604
1200
963
605
-0.2
-0.8
-4.6
-21.9
-8.7
-2.9
a This analysis is limited to the geographically contiguous lower 48 states.
ES.5 Compliance Costs
The baseline includes approximately 7 GW of operational EGU capacity designed to burn
low rank virgin coal (i.e. lignite) in 2028. All of this capacity is currently equipped with
Activated Carbon Injection (ACI) technology, which is designed to reduce mercury emissions,
and operation of this technology for compliance with existing mercury emissions limits (e.g.,
MATS and other enforceable state regulations) is reflected in the baseline. In the proposed and
more stringent modeling scenarios, each of these EGUs projected to consume lignite is assigned
an additional variable operating cost that is consistent with improvements in sorbent that EPA
assumes is necessary to achieve the lower proposed limit. In the proposed option, this additional
ES-6
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cost does not result in incremental retirements for these units, nor does it result in a significant
change to the projected generation level for these units.
In 2028, the baseline projection also includes 4.8 GW of operational coal capacity that,
based on the analysis documented in the EPA memorandum titled: "2023 Technology Review
for the Coal- and Oil-Fired EGU Source Category" EPA assumes would either need to improve
existing PM controls or install new PM controls to comply with the proposed option. The vast
majority of that 4.8 GW is currently operating existing electrostatic precipitators (ESPs) and/or
fabric filters, and nearly all of that capacity is projected to install control upgrades and remain
operational in 2028. About 500 MW of that coal steam capacity is projected to retire in response
to the proposed rule. Under the more stringent alternative, EPA assumes that 22.7 GW of
capacity that is projected to be operational in the baseline in 2028 would need to take some
compliance action in order to meet the proposed standards. About half of that capacity (11.3
GW) is projected to remain operational with the installation of PM control upgrades in 2028.
Table ES-3 below summarizes the PV and EAV of the total national compliance cost
estimates for EGUs for the proposed rule and the less and more stringent alternatives. We present
the PV of the costs over the 10-year period of 2028 to 2037. We also present the EAV, which
represents a flow of constant annual values that, had they occurred annually, would yield a sum
equivalent to the PV. The EAV represents the value of a typical cost for each year of the
analysis. These compliance cost estimates are used as a proxy for the social cost of the rule.
Section 4 reports how annual power costs are projected to change over the time period of
analysis.
Table ES-3 Total National Compliance Cost Estimates for the Proposed Rule and the
Less and More Stringent Alternatives (millions of 2019 dollars, discounted to 2023)
3% Discount Rate
7% Discount Rate
Regulatory Option
PV
EAV
PV
EAV
Proposed
330
38
230
33
Less Stringent
-45
-5.2
-31
-4.5
More Stringent
4,600
540
3,400
490
Note: Values have been rounded to two significant figures.
ES-7
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ES.6 Benefits
ES.6.1Health Benefits
ES. 6.1.1 Hazardous Air Pollutants
This proposed rule is projected to reduce emissions of mercury and non-mercury metal
HAP at a national level. Mercury emitted from U.S. EGUs can deposit to watersheds and
associated waterbodies where it can accumulate as methylmercury in fish. Methylmercury is
formed by microbial action in the top layers of sediment and soils, after mercury has precipitated
from the air and deposited into waterbodies or land. Once formed, methylmercury is taken up by
aquatic organisms and bioaccumulates up the aquatic food web. Methylmercury in fish,
originating from U.S. EGUs, is consumed both as self-caught fish by subsistence fishers and as
commercial fish by the general population. Exposure to methylmercury is known to have adverse
impacts on neurodevelopment and the cardiovascular system. Methylmercury is known to exert
some genotoxic activity and EPA has classified methylmercury as a "possible" human
carcinogen. While the screening analysis that EPA completed suggests that exposures associated
with mercury emitted from EGUs, including lignite-fired EGUs, are below levels of concern
from a public health standpoint, further reductions in these emissions should further decrease
fish burden and exposure through fish consumption including exposures to subsistence fishers.
In addition, U.S. EGUs are a major source of metallic HAP emissions including
selenium (Se), arsenic (As), chromium (Cr), nickel (Ni), and cobalt (Co), cadmium (Cd),
beryllium (Be), lead (Pb), and manganese (Mn). Some metal HAP emitted by U.S. EGUs are
known to be persistent and bioaccumulative and others have the potential to cause cancer.
Exposure to these metal HAP, depending on exposure duration and levels of exposures, is
associated with a variety of adverse health effects. The proposed controls are expected to reduce
human exposure to non-mercury metallic HAP, including carcinogens.
The projected reductions in mercury under this proposed rule are expected to reduce the
bioconcentration of methylmercury in fish. In 2020, EPA examined risk to subsistence fishers
from methylmercury exposure at a lake near three U.S. EGU lignite-fired facilities. The results
of this site-specific analysis suggest that exposure to methylmercury from lignite-fired facilities
falls below the current health benchmark for adverse effects (U.S. EPA, 2020). However, while
exposure to methylmercury from lignite-fired facilities may be below the health benchmark,
ES-8
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these emissions reductions will result in further reductions in the exposure of subsistence fishers
to methylmercury. Further, the projected reductions in non-mercury metal HAP from the use of
PM controls should help EPA reduce exposure of individuals residing near these facilities to
carcinogenic HAP.
ES. 6.1.2 Criteria Pollutants
This rule is expected to reduce emissions of direct PM2.5, NOx and SO2 throughout the
year. Because NOx and SO2 are also precursors to secondary formation of ambient PM2.5,
reducing these emissions would reduce human exposure to ambient PM2.5 throughout the year
and would reduce the incidence of PIVh.s-attributable health effects.
This proposed rule is expected to reduce ozone season NOx emissions. In the presence of
sunlight, NOx and volatile organic compounds (VOCs) can undergo a chemical reaction in the
atmosphere to form ozone. Reducing NOx emissions generally reduces human exposure to ozone
and the incidence of ozone-related health effects, though the degree to which ozone is reduced
will depend in part on local concentration levels of VOCs.
In this RIA, EPA reports estimates of the health benefits of changes in PM2.5 and ozone
concentrations. The health effect endpoints, effect estimates, benefit unit-values, and how they
were selected, are described in the Technical Support Document (TSD) titled EstimatingPM2.5-
and Ozone-Attributable Health Benefits (U.S. EPA, 2023). This document, hereafter referred to
as the "Health Benefits TSD," can be found in the docket for this rulemaking. Our approach for
updating the endpoints and to identify suitable epidemiologic studies, baseline incidence rates,
population demographics, and valuation estimates is summarized in Section 4.3.
ES.6.2 Climate Benefits
Elevated concentrations of GHGs in the atmosphere have been warming the planet,
leading to changes in the Earth's climate including changes in the frequency and intensity of heat
waves, precipitation, and extreme weather events, rising seas, and retreating snow and ice. The
well-documented atmospheric changes due to anthropogenic GHG emissions are changing the
climate at a pace and in a way that threatens human health, society, and the natural environment.
Climate change touches nearly every aspect of public welfare in the U.S. with resulting
economic costs, including: changes in water supply and quality due to changes in drought and
ES-9
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extreme rainfall events; increased risk of storm surge and flooding in coastal areas and land loss
due to inundation; increases in peak electricity demand and risks to electricity infrastructure; and
the potential for significant agricultural disruptions and crop failures (though offset to some
extent by carbon fertilization).
There will be important climate benefits associated with the CO2 emissions reductions
expected from this proposed rule. Climate benefits from reducing emissions of CO2 can be
monetized using estimates of the social cost of carbon (SC-CO2). See Section 4.4 for more
discussion of the approach to monetization of the climate benefits associated with this rule.
ES.6.3 Additional Unquantified Benefits
Data, time, and resource limitations prevented EPA from quantifying the estimated health
impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2
(independent of the role NO2 and SO2 play as precursors to PM2.5 and ozone), as well as
ecosystem effects, and visibility impairment due to the absence of air quality modeling data for
these pollutants in this analysis. Regarding HAP, data, time, and resource limitations prevent us
from quantifying potential benefits associated with ecosystem services. While all health benefits
and welfare benefits were not able to be quantified, it does not imply that there are not additional
benefits associated with reductions in exposures to HAP, ozone, PM2.5, NO2 or SO2. For a
qualitative description of these and potential water quality benefits, please see Section 4.
ES.6.4 Total Health and Climate Benefits
Table ES-4 presents the total monetized health and climate benefits for the proposed rule
and the more and less stringent alternatives. Note the less stringent regulatory alternative has no
quantified emissions reductions associated with the proposed requirements for PM CEMS and
the removal of startup definition number two. As a result, there are no quantified benefits
associated with this regulatory option.
ES-10
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Table ES-4 Monetized Health Benefits and Climate Benefits for the Proposed Rule from
2028 through 2037 (millions of 2019 dollars, discounted to 2023)a
All Benefits Calculated using 3% Discount Rate
PM2.5 and 03-related
Climate
Total
Health Benefits b
Benefitsc
Benefits d'e
Regulatory Option
PV
EAV
PV EAV
PV
EAV
Proposed
1,900
220
1,400 170
3,300
390
Less Stringent
0.0
0.0
O
O
O
O
0.0
0.0
More Stringent
11,000
1,300
3,200 380
14,000
1,700
Health Benefits Calculated using 7% Discount Rate,
Climate Benefits Calculated using 3% Discount Rate
PM2.5 and 03-related
Climate
Total
Regulatory Option
Health Benefits b
Benefitsc
Benefits d'e
Proposed
1,200
170
1,400 170
2,600
330
Less Stringent
0.0
0.0
O
O
O
O
0.0
0.0
More Stringent
7,100
1,000
3,200 380
10,000
1,400
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 7-1, Table 7-2, and Table 7-3. Monetized benefits include those related to
public health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with
several point estimates.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits
include important benefits from reductions in mercury and non-mercury metal HAP.
e For discussions of the uncertainty associated with these health benefits estimates, see Section 4.3.8. See Section
4.3.10 for a discussion of the uncertainties associated with the climate benefit estimates.
ES.7 Environmental Justice Impacts
Executive Order 12898 directs the EPA to identify the populations of concern who are
most likely to experience unequal burdens from environmental harms; specifically, minority
populations, low-income populations, and Indigenous peoples.3 Additionally, Executive Order
13985 is intended to advance racial equity and support underserved communities through federal
government actions.4 The EPA defines environmental justice (EJ) as the fair treatment and
meaningful involvement of all people regardless of race, color, national origin, or income, with
respect to the development, implementation, and enforcement of environmental laws,
regulations, and policies. The EPA further defines the term fair treatment to mean that "no group
of people should bear a disproportionate burden of environmental harms and risks, including
those resulting from the negative environmental consequences of industrial, governmental, and
3 59 FR 7629, February 16, 1994.
4 86 FR 7009, January 20, 2021.
ES-11
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commercial operations or programs and policies."5 In recognizing that minority and low-income
populations often bear an unequal burden of environmental harms and risks, the EPA continues
to consider ways of protecting them from adverse public health and environmental effects of air
pollution.
Environmental justice (EJ) concerns for each rulemaking are unique and should be
considered on a case-by-case basis, and EPA's EJ Technical Guidance (2015) states that "[t]he
analysis of potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern in the baseline?
2. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern for the regulatory option(s) under
consideration?
3. For the regulatory option(s) under consideration, are potential EJ concerns created or
mitigated compared to the baseline?"
To address these questions, EPA developed an analytical approach that considers the
purpose and specifics of the rulemaking, as well as the nature of known and potential
disproportionate and adverse exposures and impacts. For the rule, we quantitatively evaluate 1)
the proximity of affected facilities to potentially vulnerable and/or overburdened populations for
consideration of local pollutants impacted by this rule but not modeled here (Section 6.3) and 2)
the distribution of ozone and PM2.5 concentrations in the baseline and changes due to the
proposed rulemaking across different demographic groups on the basis of race, ethnicity, poverty
status, employment status, health insurance status, age, sex, educational attainment, and degree
of linguistic isolation (Section 6.5). We also qualitatively discuss potential EJ HAP and climate
impacts (Sections 6.3 and 6.6). Each of these analyses depends on mutually exclusive
assumptions, was performed to answer separate questions, and is associated with unique
limitations and uncertainties.
Baseline demographic proximity analyses provide information as to whether there may
be potential EJ concerns associated with environmental stressors, such as noise, traffic, or SO2
emitted from sources affected by the regulatory action for certain population groups of concern
(Section 6.4). The baseline demographic proximity analyses examined the demographics of
5 https://www.epa.gov/environmentaljustice.
ES-12
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populations living within 10 km of the following sources: lignite plants with units potentially
subject to the proposed mercury standard revision, coal plants with units potentially subject to
the proposed filterable PM standard revision, and coal plants with units potentially subject to the
alternate filterable PM standard revision. The baseline analysis indicates that on average the
percentage of the population living within 10 km of coal plants potentially subject to the
proposed or alternate filterable PM standards have a higher percentage of people living below
two times the poverty level than the national average. In addition, on average the percentage of
the Native American population living within 10 km of lignite plants potentially subject to
proposed mercury standard is higher than the national average. Relating these results to question
1, above, we conclude that there may be potential EJ concerns associated with directly emitted
pollutants that are affected by the regulatory action (e.g., SO2) for certain population groups of
concern in the baseline. However, as proximity to affected facilities does not capture variation in
baseline exposure across communities, nor does it indicate that any exposures or impacts will
occur, these results should not be interpreted as a direct measure of exposure or impact.
As HAP exposure results generated as part of the 2020 Residual Risk analysis were
below both the presumptive acceptable cancer risk threshold and the RfD, and this proposed
regulation should further reduce exposure to HAP, there are no 'disproportionate and adverse
effects' of potential EJ concern. Therefore, we did not perform a quantitative EJ assessment of
HAP risk.
In contrast, ozone and PM2.5 emission changes are also expected from this action and
exposure analyses that evaluate demographic variables are better able to evaluate any potentially
disproportionate pollution impacts of this rulemaking. The baseline ozone and PM2.5 exposure
analyses respond to question 1 from EPA's EJ Technical Guidance document more directly than
the proximity analyses, as they evaluate a form of the environmental stressor primarily affected
by the regulatory action (see Section 6.5). Baseline ozone and PM2.5 exposure analyses show that
certain populations, such as Hispanics, Asians, those linguistically isolated, those less educated,
and children may experience disproportionately higher ozone and PM2.5 exposures as compared
to the national average. American Indians may also experience disproportionately higher ozone
concentrations than the reference group. Therefore, there likely are potential EJ concerns
associated with environmental stressors affected by the regulatory action for population groups
of concern in the baseline.
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Finally, we evaluate how post-policy regulatory alternatives of this proposed rulemaking
are expected to differentially impact demographic populations, informing questions 2 and 3 from
EPA's EJ Technical Guidance with regard to ozone and PM2.5 exposure changes. Due to the
small magnitude of the exposure changes across population demographics associated with the
rulemaking relative to the magnitude of the baseline disparities, we infer that disparities in the
ozone and PM2.5 concentration burdens are likely to remain after implementation of the
regulatory action or alternatives under consideration. This is due to the small magnitude of the
concentration changes associated with this rulemaking across population demographic groups,
relative to the magnitude of the baseline disparities (question 2). Also due to the very small
differences observed in the distributional analyses of post-policy ozone and PM2.5 exposure
impacts across population groups, we do not find evidence that potential EJ concerns related to
ozone and PM2.5 concentrations will be created or mitigated as compared to the baseline
(question 3).
ES.8 Comparison of Benefits and Costs
All benefits analyses, and most cost analyses, begin in the year 2028, the compliance year
for the proposed standards. In this RIA, the regulatory impacts are evaluated for the specific
years of 2028, 2030, and 2035. Comparisons of benefits to costs for these snapshot years are
presented in Section 7.3 of this RIA. Here we present the PV of costs, benefits, and net benefits,
calculated for the years 2028 to 2037 from the perspective of 2023, using both a three percent
and seven percent end-of-period discount rate as directed by OMB's Circular A-4. All dollars are
in 2019 dollars. We also present the EAV, which represents a flow of constant annual values
that, had they occurred in each year from 2028 to 2037, would yield a sum equivalent to the PV.
The EAV represents the value of a typical cost or benefit for each year of the analysis, in contrast
to the year-specific estimates reported in the costs and benefits sections of this RIA. The
comparison of benefits and costs in PV and EAV terms for the proposed rule and less and more
stringent regulatory options can be found in Table ES-5. Estimates in the tables are presented as
rounded values. Note the less stringent regulatory alternative has no quantified emissions
reductions associated with the proposed requirements for PM CEMS and the removal of startup
definition number two. As a result, there are no quantified benefits associated with this
regulatory option.
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Table ES-5 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less
and More Stringent Alternatives (millions of 2019 dollars, discounted to 2023) a'b
Values Calculated using 3% Discount Rate
PM2.5 and O3-
related Health Climate Compliance Net
Benefitsb Benefitsc Costs Benefitsd
Regulatory Option
PV
EAV
PV
EAV
PV
EAV
PV EAV
Proposed
1,900
220
1,400
170
330
38
3,000 350
Less Stringent
0.0
0.0
0.0
0.0
-45
-5.2
45 5.2
More Stringent
11,000
1,300
3,200
380
4,600
540
9,800 1,100
Compliance Costs and Health Benefits Calculated using 7% Discount Rate,
Climate Benefits Calculated using 3% Discount Rate
PM2.5
and O3-
related Health
Climate
Compliance
Net
Benefits b
Benefits
C
Costs
Benefits d
Regulatory Option
PV
EAV
PV
EAV
PV
EAV
PV EAV
Proposed
1,200
170
1,400
170
230
33
2,400 300
Less Stringent
0.0
0.0
0.0
0.0
-31
-4.5
31 4.5
More Stringent
7,100
1,000
3,200
380
3,400
490
6,900 900
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b The health benefits estimates use the larger of the two benefits estimates presented in Table 7-1, Table 7-2, and
Table 7-3. Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates.
0 Climate benefits are based on reductions in CO2 emissions and are calculated using four different estimates of the
social cost of carbon dioxide (SC-CO2): model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th
percentile at 3 percent discount rate. The 95th percentile estimate is included to provide information on potentially
higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount
rate. For the presentational purposes of this table, we show the climate benefits associated with the average SC-CO2
at a 3 percent discount rate, but the Agency does not have a single central SC-CO2 point estimate. Climate benefits
in this table are discounted using a 3 percent discount rate to obtain the PV and EAV estimates in the table. We
emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. Section
4.4 of the RIA presents estimates of the projected climate benefits of this proposal using all four rates. We note that
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is
warranted when discounting intergenerational impacts.
d Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit
estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal
HAP emissions and from the increased transparency and accelerated identification of anomalous emission
anticipated from requiring CEMS.
The quantitative estimates of net benefits presented in this section are underestimated
because important categories of benefits, including benefits from reducing mercury and non-
mercury metal HAP emissions and the increased transparency and accelerated identification of
anomalous emission anticipated from requiring PM CEMS, were not monetized and are therefore
not directly reflected in the monetized benefit-cost comparisons. We nonetheless consider these
potential impacts in our evaluation of the net benefits of the rule in that, if we were able to
monetize these impacts, the proposal would have greater net benefits.
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ES.9 References
U.S. EPA. (2014). Guidelines for Preparing Economic Analyses. (EPA 240-R-10-001).
Washington DC: U.S. Environmental Protection Agency, Office of Policy, National
Center for Environmental Economics. Available at: https://www.epa.gov/environmental-
economics/guidelines-preparing-economic-analyses
U.S. EPA. (2015). Guidance on Considering Environmental Justice During the Development of
Regulatory Actions. Available at: https://www.epa.gov/sites/default/files/2015-
06/documents/considering-ej-in-rulemaking-guidefinal.pdf
U.S. EPA. (2020). Residual Risk Assessment for the Coal- and Oil-Fir ed EGU Source Category
in Support of the 2020 Risk and Technology Review Final Rule. Research Triangle Park,
NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards. Available at: https://www.regulations.gov/document/EPA-HQ-OAR-2018-
0794-4553
U.S. EPA. (2023). EstimatingPM2.5- and Ozone-Attributable Health Benefits. Research Triangle
Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Health and Environmental Impact Division. Available at:
https://www.regulations.gov/docket/EPA-HQ-OAR-2018-0794
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1 INTRODUCTION AND BACKGROUND
1.1 Introduction
On January 20, 2021, President Biden signed E.O. 13990, "Protecting Public Health and
the Environment and Restoring Science to Tackle the Climate Crisis" (86 FR 7037; January 25,
2021). The executive order instructs EPA, among other things, to review the 2020 final action
titled, "National Emission Standards for Hazardous Air Pollutants: Coal- and Oil-Fired Electric
Utility Steam Generating Units-Reconsideration of Supplemental Finding and Residual Risk and
Technology Review" (85 FR 31286; May 22, 2020) (2020 Final Action) and to consider
publishing a notice of proposed rulemaking suspending, revising, or rescinding that action. The
2020 Final Action included a finding that it is not appropriate and necessary to regulate coal and
oil-fired EGUs under CAA section 112 as well as the RTR for the MATS rule. The results of
EPA's review of the appropriate and necessary finding were proposed on February 9, 2022 (87
FR 7624) and finalized on March 6, 2023 (88 FR 13956). This action presents the proposed
results of EPA's review of the MATS RTR, as directed by E.O. 13990.
Several statutes and executive orders apply to federal rulemakings. In accordance with
E.O. 12866 and E.O. 13563 and the guidelines of OMB Circular A-4, the RIA analyzes the
benefits and costs associated with the projected emissions reductions under the proposed rule.
OMB Circular A-4 requires analysis of one potential regulatory option more stringent and one
less stringent than the rule under examination, so this RIA evaluates the benefits, costs, and
impacts of a more and a less stringent alternative to the selected alternative in this proposal. The
benefits and costs of the proposed rule and regulatory alternatives are presented for the 2028 to
2037 time period. The estimated monetized benefits are those health benefits expected to arise
from reduced PM2.5 and ozone concentrations and the climate benefits from reductions in GHGs.
Several categories of benefits remain unmonetized including important benefits from reductions
in mercury and non-mercury metal HAP emissions. The estimated monetized costs for EGUs are
the costs of installing and operating controls and the increased costs of producing electricity.
Unquantified benefits and costs are described qualitatively. This section contains background
information relevant to the rule and an outline of the sections of this RIA.
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1.2 Legal and Economic Basis for Rulemaking
In this section, we summarize the statutory requirements in the CAA that serve as the
legal basis for the proposed rule and the economic theory that supports environmental regulation
as a mechanism to enhance social welfare. The CAA requires EPA to prescribe regulations for
new and existing sources. In turn, those regulations attempt to address negative externalities
created when private entities fail to internalize the social costs of air pollution.
1.2.1 Statutory Requirement
The statutory authority for this action is provided by sections 112 and 301 of the CAA, as
amended (42 U.S.C. 7401 et seq.). Section 112 of the CAA establishes a two-stage regulatory
process to develop standards for emissions of HAP from stationary sources. Generally, the first
stage involves establishing technology-based standards and the second stage involves evaluating
those standards that are based on maximum achievable control technology (MACT) to determine
whether additional standards are needed to address any remaining risk associated with HAP
emissions. This second stage is commonly referred to as the "residual risk review." In addition to
the residual risk review, the CAA also requires EPA to review standards set under CAA section
112 no less than every eight years and revise the standards as necessary taking into account any
"developments in practices, processes, or control technologies." This review is commonly
referred to as the "technology review," and is the subject of this proposal.
1.2.2 Regulated Pollu tants
For coal-fired EGUs, the 2012 MATS rule established standards to limit emissions of
mercury, acid gas HAP, non-mercury HAP metals (e.g., nickel, lead, chromium), and organic
HAP (e.g., formaldehyde, dioxin/furan). Standards for hydrochloric acid (HC1) serve as a
surrogate for the acid gas HAP, with an alternate standard for sulfur dioxide (SO2) that may be
used as a surrogate for acid gas HAP for those coal-fired EGUs with flue gas desulfurization
(FGD) systems and SO2 CEMS installed and operational. Standards for filterable particulate
matter serve as a surrogate for the non-mercury HAP metals, with standards for total non-
mercury HAP metals and individual non-mercury HAP metals provided as alternative equivalent
standards. Work practice standards limit formation and emission of the organic HAP.
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For oil-fired EGUs, the 2012 MATS rule establishes standards to limit emissions of HC1
and hydrogen fluoride (HF), total HAP metals (e.g., mercury, nickel, lead), and organic HAP
(e.g., formaldehyde, dioxin/furan). Standards for filterable PM serve as a surrogate for total HAP
metals, with standards for total HAP metals and individual HAP metals provided as alternative
equivalent standards. Work practice standards limit formation and emission of the organic HAP.
1.2.2.1 Definition of Affected Source
The source category that is the subject of this proposal is Coal- and Oil-Fired EGUs
regulated under 40 CFR 63, subpart UUUUU. The North American Industry Classification
System (NAICS) codes for the Coal- and Oil-fired EGU industry are 221112, 221122, and
921150. This list of categories and NAICS codes is not intended to be exhaustive, but rather
provides a guide for readers regarding the entities that this proposed action is likely to affect. The
proposed standards, once promulgated, will be directly applicable to the affected sources.
Federal, state, local, and tribal government entities that own and/or operate EGUs subject to 40
CFR part 63, subpart UUUUU would be affected by this proposed action. The Coal- and Oil-
Fired EGU source category was added to the list of categories of major and area sources of HAP
published under section 112(c) of the CAA on December 20, 2000 (65 FR 79825). CAA section
112(a)(8) defines an EGU as: any fossil fuel fired combustion unit of more than 25 megawatts
that serves a generator that produces electricity for sale. A unit that cogenerates steam and
electricity and supplies more than one-third of its potential electric output capacity and more than
25 megawatts electrical output to any utility power distribution system for sale is also considered
an EGU.
1.2.3 The Need for Air Emissions Regulation
OMB Circular A-4 indicates that one of the reasons a regulation may be issued is to
address a market failure. The major types of market failure include externalities, market power,
and inadequate or asymmetric information. Correcting market failures is one reason for
regulation; it is not the only reason. Other possible justifications include improving the function
of government, correcting distributional unfairness, or securing privacy or personal freedom.
Environmental problems are classic examples of externalities - uncompensated benefits
or costs imposed on another party as a result of one's actions. For example, the smoke from a
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factory may adversely affect the health of local residents and soil the property in nearby
neighborhoods. For the proposed regulatory action analyzed in this RIA, the good produced is
electricity from coal- and oil-fired EGUs. If these electricity producers pollute the atmosphere
when generating power, the social costs will not be borne exclusively by the polluting firm but
rather by society as a whole. Thus, the producer is imposing a negative externality, or a social
cost of emissions, on society. The equilibrium market price of electricity may fail to incorporate
the full opportunity cost to society of these products. Consequently, absent a regulation on
emissions, producers will not internalize the social cost of emissions and social costs will be
higher as a result. The proposed regulation will work towards addressing this market failure by
causing affected producers to begin internalizing the negative externality associated with HAP
emissions from electricity generation by coal- and oil-fired EGUs.
1.3 Overview of Regulatory Impact Analysis
1.3.1 Regulatory Options
This RIA focuses on four proposed amendments to the MATS rule, which are described
in more detail in this section. We vary these four proposed requirements in order to craft a set of
three regulatory options to be analyzed in this RIA.
1.3.1.1 Filterable Particulate Matter Standards for Existing Coal-fired EGUs
Existing coal-fired EGUs are subject to numeric emission limits for filterable PM, a
surrogate for the total non-mercury HAP metals.6 MATS currently requires existing coal-fired
EGUs to meet a filterable particulate matter emission standard of 0.030 pounds per million
British thermal units (lb/MMBtu) of heat input. The standards for filterable PM serve as a
surrogate for standards for non-mercury HAP metals. After reviewing updated information on
the current emission levels of filterable PM from existing coal-fired EGUs and the costs of
6 As described in section III of the preamble to this proposed rule, EGUs in six subcategories are subject to numeric
emission limits for specific HAP or fPM, a surrogate for the total non-mercury HAP metals. The fPM was chosen as
a surrogate in the original rulemaking because the non-mercury HAP metals are predominantly a component of PM,
and control of PM will also result in co-reduction of non-mercury HAP metals. Additionally, not all fuels emit the
same type and amount of metallic HAP, but most generally emit PM that include some amount and combination of
all the metallic HAP. Lastly, the use of fPM as a surrogate eliminates the cost of performance testing to comply with
numerous standards for individual non-mercury metal HAP (Docket ID No. EPA-HQ-OAR-2009-0234). For these
reasons, the EPA focused its review on the fPM emissions of coal-fired EGUs as a surrogate for the non-mercury
metal HAP.
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meeting a standard more stringent than 0.030 lb/MMBtu, EPA is proposing to revise the
filterable PM emission standard for existing coal-fired EGUs to 0.010 lb/MMBtu. EPA also
solicits comment on requiring existing coal-fired EGUs to meet a filterable PM standard of 0.006
lb/MMBtu.
1.3.1.2 Mercury Emission Standard for Lignite-fired EGUs
EPA is also proposing to revise the mercury emission standard for lignite-fired EGUs.
Currently, lignite-fired EGUs must meet a mercury emission standard of 4.0 pounds per trillion
British thermal units (lb/TBtu) or 4.0E-2 pounds per gigawatt hour (lb/GWh). EPA recently
collected information on current emission levels and mercury emission controls for lignite-fired
EGUs using the authority provided under CAA section 114.7 That information showed that many
units are able to achieve a mercury emission rate that is much lower than the current standard,
and there are cost-effective control technologies and methods of operation that are available to
achieve a more stringent standard. EPA is proposing that lignite-fired EGUs meet the same
standard as EGUs firing other types of coal, 1.2 lb/TBtu or 1.3E-2 lb/GWh.
1.3.1.3 Require that all coal-fired EGUs demonstrate compliance with the filterable PM
emission standard by using PM CEMS.
In addition to revising the PM emission standard for existing coal-fired EGUs, EPA is
proposing a revision to the requirements for demonstrating compliance with the PM emission
standard for coal-fired EGUs. Currently, EGUs that are not part of the low emitting EGU (LEE)
program can demonstrate compliance with the filterable PM standard either by conducting
performance testing quarterly or through the use of PM CEMS. After considering updated
information on the costs for performance testing compared to the cost of PM CEMS and
capabilities of PM CEMS measurement abilities, as well as the benefits of using PM CEMS,
which include increased transparency and accelerated identification of anomalous emissions,
EPA is proposing to require that all coal-fired EGUs demonstrate compliance with the PM
emission standard by using PM CEMS.
7 For further information, see EPA memorandum titled: "2023 Technology Review for the Coal- and Oil-Fired EGU
Source Category" which is available in the docket
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1.3.1.4 Startup Definitions
Finally, EPA is proposing to remove one of the two options for defining the startup
period for EGUs. The first option defines startup as either the first-ever firing of fuel in a boiler
for the purpose of producing electricity, or the firing of fuel in a boiler after a shutdown event for
any purpose. Startup ends when any of the steam from the boiler is used to generate electricity
for sale over the grid or for any other purpose (including on-site use). In the second option,
startup is defined as the period in which operation of an EGU is initiated for any purpose. Startup
begins with either the firing of any fuel in an EGU for the purpose of producing electricity or
useful thermal energy (such as heat or steam) for industrial, commercial, heating, or cooling
purposes (other than the first-ever firing of fuel in a boiler following construction of the boiler)
or for any other purpose after a shutdown event. Startup ends four hours after the EGU generates
electricity that is sold or used for any other purpose (including on-site use), or four hours after
the EGU makes useful thermal energy (such as heat or steam) for industrial, commercial,
heating, or cooling purposes, whichever is earlier. EPA is proposing to remove the second
option, which is currently being used by fewer than 10 EGUs.
1.3.1.5 Summary of Proposed Regulatory Options Examined in this RIA
Table 1- summarizes how we have structured the regulatory options to be analyzed in this
RIA. The proposed regulatory option includes the proposed amendments just discussed in this
section: the proposed revision to the filterable PM standard to 0.010 lb/MMBtu, in which
filterable PM is a surrogate for non-mercury metal HAP, the proposed revision to the mercury
standard for lignite-fired EGUs to 1.2 lb/TBtu, the proposal to require PM CEMS to demonstrate
compliance, and the removal of the startup definition number two. The more stringent regulatory
option examined in this RIA tightens the proposed revision to the filterable PM standard to 0.006
lb/MMBtu. Note EPA is soliciting comment on this more stringent filterable PM standard. The
other three proposed amendments are not changed in the more stringent regulatory option
examined in this RIA. Finally, the less stringent regulatory option examined in this RIA assumed
the PM and mercury limits remain unchanged and examines just the proposed PM CEMS
requirement and removal of startup definition number two.
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Table 1-1 Summary of Proposed Regulatory Options Examined in this RIA
Regulatory Options Examined in this RIA
Provision
Less Stringent
Proposed
More Stringent
Filterable PM
Standard
(Surrogate
Standard for Non-
Retain existing filterable
PM standard of 0.030
lb/MMBtu
Revised filterable PM
standard of 0.010
lb/MMBtu
Revised filterable PM
standard of 0.006
lb/MMBtu
Mercury Standard
Retain mercury standard
for lignite-fired EGUs of
4.0 lb/TBtu
Revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
Revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
Continuous
Emissions
Monitoring Systems
(PM CEMS)
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Startup definition
Remove startup definition
#2
Remove startup definition
#2
Remove startup definition
#2
1.3.2 Baseline and Analysis Years
The impacts of proposed regulatory actions are evaluated relative to a baseline that
represents the world without the proposed action. This version of the model ("EPA's Post-IRA
2022 Reference Case") used for the baseline in this RIA includes recent updates to state and
federal legislation affecting the power sector, including Public Law 117-169, 136 Stat. 1818
(August 16, 2022), commonly known as the Inflation Reduction Act of 2022 (IRA). The
modeling documentation includes a summary of all legislation reflected in this version of the
model as well as a description of how that legislation is implemented in the model.8 Also, see
Section 3.3 for additional detail about the power sector baseline for this RIA.
All benefit analysis, and most cost analysis, begins in the year 2028, the compliance year
for the proposed standards. In addition, the regulatory impacts are evaluated for the specific
analysis years of 2030 and 2035. These results are used to estimate the PV and EAV of the 2028
through 2037 period.
1.4 Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining sections:
8 See document titled "Documentation for EPA's Power Sector Modeling Platform v6 Using the Integrated Planning
Model Post-IRA 2022 Reference Case", which is available in the docket for this action.
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• Section 2: Power Sector Profile. This section describes the electric power sector in detail.
• Section 3: Cost, Emissions, and Energy Impacts. The section summarizes the projected
compliance costs and other energy impacts associated with the regulatory options.
• Section 4: Benefits Analysis. The section presents the projected health and environmental
benefits of reductions in emissions of HAP, direct PM2.5, and PM2.5 and ozone precursors
and the climate benefits of CO2 emissions reductions across regulatory options. Potential
benefits to drinking water quality and quantity are also discussed.
• Section 5: Economic Impacts. The section includes a discussion of potential small entity,
economic, and labor impacts.
• Section 6: Environmental Justice Impacts. This section includes an assessment of
potential impacts to potential EJ populations.
• Section 7: Comparison of Benefits and Costs. The section compares of the total projected
benefits with total projected costs and summarizes the projected net benefits of the three
regulatory options examined. The section also includes a discussion of potential benefits
that EPA is unable to quantify and monetize.
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2 INDUSTRY PROFILE
2.1 Background
In the past decade there have been significant structural changes in both the mix of
generating capacity and in the share of electricity generation supplied by different types of
generation. These changes are the result of multiple factors in the power sector, including normal
replacements of older generating units with new units, changes in the electricity intensity of the
U.S. economy, growth and regional changes in the U.S. population, technological improvements
in electricity generation from both existing and new units, changes in the prices and availability
of different fuels, and substantial growth in electricity generation by renewable and
unconventional methods. Many of these trends will continue to contribute to the evolution of the
power sector. The evolving economics of the power sector, specifically the increased natural gas
supply and subsequent relatively low natural gas prices, have resulted in more natural gas being
used as base load energy in addition to supplying electricity during peak load. Additionally, rapid
growth in the penetration of renewables has led to their now constituting a significant share of
generation. This section presents data on the evolution of the power sector from 2014 through
2020. Projections of future power sector behavior and the impact of this proposed rule are
discussed in more detail in Section 3 of this RIA.
2.2 Power Sector Overview
The production and delivery of electricity to customers consists of three distinct
segments: generation, transmission, and distribution.
2.2.1 Generation
Electricity generation is the first process in the delivery of electricity to consumers. There
are two important aspects of electricity generation: capacity and net generation. Generating
Capacity refers to the maximum amount of production an EGU is capable of producing in a
typical hour, typically measured in megawatts (MW) for individual units, or gigawatts (1 GW =
1,000 MW) for multiple EGUs. Electricity Generation refers to the amount of electricity actually
produced by an EGU over some period of time, measured in kilowatt-hours (kWh) or gigawatt-
hours (1 GWh = 1 million kWh). Net Generation is the amount of electricity that is available to
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the grid from the EGU (i.e., excluding the amount of electricity generated but used within the
generating station for operations). Electricity generation is most often reported as the total annual
generation (or some other period, such as seasonal). In addition to producing electricity for sale
to the grid, EGUs perform other services important to reliable electricity supply, such as
providing backup generating capacity in the event of unexpected changes in demand or
unexpected changes in the availability of other generators. Other important services provided by
generators include facilitating the regulation of the voltage of supplied generation.
Individual EGUs are not used to generate electricity 100 percent of the time. Individual
EGUs are periodically not needed to meet the regular daily and seasonal fluctuations of
electricity demand. Furthermore, EGUs relying on renewable resources such as wind, sunlight,
and surface water to generate electricity are routinely constrained by the availability of adequate
wind, sunlight, or water at different times of the day and season. Units are also unavailable
during routine and unanticipated outages for maintenance. These factors result in the mix of
generating capacity types available (e.g., the share of capacity of each type of EGU) being
substantially different than the mix of the share of total electricity produced by each type of EGU
in a given season or year.
Most of the existing capacity generates electricity by creating heat to create high pressure
steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined
cycle (NGCC) units have two generating components operating from a single source of heat. The
first cycle is a gas-fired turbine, which generates electricity directly from the heat of burning
natural gas. The second cycle reuses the waste heat from the first cycle to generate steam, which
is then used to generate electricity from a steam turbine. Other EGUs generate electricity by
using water or wind to rotate turbines, and a variety of other methods including direct
photovoltaic generation also make up a small, but growing, share of the overall electricity
supply. The generating capacity includes fossil-fuel-fired units, nuclear units, and hydroelectric
and other renewable sources (see Table 2-1). Table 2-1 also shows the comparison between the
generating capacity over the 2015 to 2021 period.
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In 2021 the power sector comprised a total capacity9 of 1,179 GW, an increase of 105
GW (or 10 percent) from the capacity in 2015 (1,074 GW). The largest change over this period
was the decline of 70 GW of coal capacity, reflecting the retirement/rerating of over a third of
the coal fleet. This reduction in coal capacity was offset by an increase in natural gas capacity of
52 GW, and an increase in solar (48 GW) and wind (60 GW) capacity over the same period.
Additionally, significant amounts of distributed solar (23 GW) were also added.
Table 2-1 Total Net Summer Electricity Generating Capacity by Energy Source, 2015
and 2021
2015 2021 Change Between '15 and '21
Energy Source
Net Summer
Capacity
(GW)
% Total
Capacity
Net Summer
Capacity
(GW)
% Total
Capacity
% Increase
Capacity
Change
(GW)
Coal
280
26%
210
18%
-25%
-70
Natural Gas
439
41%
492
42%
12%
52
Nuclear
99
9%
96
8%
-3%
-3
Hydro
102
10%
103
9%
1%
1
Petroleum
37
3%
28
2%
-23%
-9
Wind
73
7%
133
11%
83%
60
Solar
14
1%
62
5%
350%
48
Distributed Solar
10
1%
33
3%
238%
23
Other Renewable
17
2%
15
1%
-10%
-2
Misc
4
0%
8
1%
91%
4
Total
1,074
100%
1,179
100%
10%
105
Note: This table presents generation capacity. Actual net generation is presented in Table 2-2.
Source: EIA. Electric Power Annual 2021, Tables 4.2.A
In 2021, electric generating sources produced a net 4,157 trillion kWh (TWh) to meet
national electricity demand, which was around 2 percent higher than 2015. As presented in Table
2-1, 59 percent of electricity in 2021 was produced through the combustion of fossil fuels,
primarily coal and natural gas, with natural gas accounting for the largest single share. The total
generation share from fossil fuels in 2021 (60 percent) was 11 percent less than the share in 2010
(69 percent). Moreover, the share of fossil generation supplied by coal fell from 65 percent in
2010 to 36 percent by 2021, while the share of fossil generation supplied by natural gas rose
9 This includes generating capacity at EGUs primarily operated to supply electricity to the grid and combined heat
and power facilities classified as Independent Power Producers (IPP) and excludes generating capacity at
commercial and industrial facilities that does not operate primarily as an EGU. Natural Gas information in this
section (unless otherwise stated) reflects data for all generating units using natural gas as the primary fossil heat
source. This includes Combined Cycle Combustion Turbine, Gas Turbine, steam, and miscellaneous (< 1 percent).
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from 35 percent to 64 percent over the same period. In absolute terms, coal generation declined
by 51 percent, while natural gas generation increased by 60 percent. This reflects both the
increase in natural gas capacity during that period as well as an increase in the utilization of new
and existing gas EGUs during that period. The combination of wind and solar generation also
grew from 2 percent of the mix in 2010 to 13 percent in 2021.
Table 2-2 Net Generation in 2015 and 2021 (Trillion kWh = TWh)
2015
2021
Change Between '15
and '21
Energy Source
Net
Generation
(TWh)
Fuel Source
Share
Net
Generation
(TWh)
Fuel Source
Share
% Increase
Generation
Change
(TWh)
Coal
1352
33%
898
22%
-34%
-455
Natural Gas
1335
33%
1579
38%
18%
246
Nuclear
797
19%
778
19%
-2%
-19
Hydro
249
6%
252
6%
1%
2
Petroleum
28
1%
19
0%
-32%
-9
Wind
191
5%
378
9%
98%
187
Solar
25
1%
115
3%
363%
90
Distributed Solar
14
0%
49
1%
248%
35
Other Renewable
80
2%
70
2%
-12%
-9
Misc
27
1%
24
1%
-13%
-4
Total
4,092
100%
4,157
100%
2%
66
Source: EIA. Electric Power Annual 2021, Tables 3. l.A and 3. l.B
The average age of coal-fired power plants that have retired between 2015 and 2021 is
over 50 years. Older power plants tend to become uneconomic over time as they become more
costly to maintain and operate, and as newer and more efficient alternative generating
technologies are built. As a result, coal's share of total U.S. electricity generation has been
declining for over a decade, while generation from natural gas and renewables has increased
significantly.10 As shown in Figure 2-1 below, 65 percent of the coal fleet in 2021 had an average
age of over 40 years.
10 EIA, Today in Energy (April 17, 2017) available at https://www.cia.gov/today incncrgy/dctail.php?id=30812
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Figure 2-1 National Coal-fired Capacity (GW) by Age of EGU, 2021
Source: NEEDS v6
Coal-fired and nuclear generating units have historically supplied "base load" electricity,
the portion of electricity loads that are continually present and typically operate throughout all
hours of the year. Although much of the coal fleet has historically operated as base load, there
can be notable differences across various facilities (see Table 2-3). For example, coal-fired units
less than 100 megawatts (MW) in size comprise 18 percent of the total number of coal-fired
units, but only 2 percent of total coal-fired capacity. Gas-fired generation is better able to vary
output, and is therefore the primary option used to meet the variable portion of the electricity
load. Gas-fired generation has historically supplied "peak" and "intermediate" power, when there
is increased demand for electricity (for example, when businesses operate throughout the day or
when people return home from work and run appliances and heating/air-conditioning), versus
late at night or very early in the morning, when demand for electricity is reduced. Moreover, as
shown in Figure 2-2, average annual coal capacity factors have declined from 67 percent to 49
percent over the 2010-2021 period, indicating that a larger share of units are operating in non-
baseload fashion. Over the same period, natural gas combined cycle capacity factors have risen
from an annual average of 44 percent to 55 percent.
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70%
60%
£ 50%
u
re
LL
£ 40%
u
H3
Q_
ro
u 30%
ro
c
< 20%
10%
0%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
— — — Coal — — — Natural Gas Combined Cycle
Figure 2-2 Average Annual Capacity Factor by Energy Source
Source: EIA. Electric Power Annual 2021, Table 4.08.A
Table 2-3 also shows comparable data for the capacity and age distribution of natural gas
units. Compared with the fleet of coal EGUs, the natural gas fleet of EGUs is generally smaller
and newer. While 67 percent of the coal EGU fleet capacity is over 500 MW per unit, 75 percent
of the gas fleet is between 50 and 500 MW per unit.
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Table 2-3 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average
Heat Rate in 2020
Unit Size
Grouping
(MW)
No. Units
% of All
Units
Avg. Age
Avg. Net
Summer
Capacity
(MW)
Total Net
Summer
Capacity
(MW)
% Total
Capacity
Avg. Heat
Rate
(Btu/kWh)
COAL
0-24
31
6%
49
11
351
0%
11,379
25-49
32
6%
35
36
1,150
1%
11,541
50-99
24
5%
39
76
1,823
1%
11,649
100 - 149
36
7%
50
122
4,388
2%
11,167
150-249
61
12%
52
197
12,027
6%
10,910
250 - 499
132
26%
42
372
49,090
24%
10,700
500 - 749
138
27%
41
609
83,978
40%
10,315
750 - 999
50
10%
38
827
41,345
20%
10,135
1000 - 1500
11
2%
43
1,264
13,903
7%
9,834
Total Coal
515
100%
43
404
208,056
100%
10,718
NATURAL
GAS
0-24
4,329
54%
31
5
21,626
4%
13,244
25-49
932
12%
26
41
38,089
8%
11,759
50-99
1,018
13%
27
71
72,744
15%
12,163
100 - 149
410
5%
23
126
51,567
10%
9,447
150-249
1,041
13%
18
179
186,494
37%
8,226
250 - 499
293
4%
21
332
97,244
19%
8,293
500 - 749
37
0%
38
592
21,910
4%
10,384
750 - 999
10
0%
46
828
8,278
2%
11,294
1000 - 1500
1
0%
0
1,060
1,060
0%
7,050
Total Gas
8,060
100%
28
62
499,012
100%
11,900
Source: National Electric Energy Data System (NEEDS) v.6
Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a
generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel
efficiency.
In terms of the age of the generating units, almost 50 percent of the total coal generating
capacity has been in service for more than 40 years, while nearly 50 percent of the natural gas
capacity has been in service less than 15 years. Figure 2-3 presents the cumulative age
distributions of the coal and gas fleets, highlighting the pronounced differences in the ages of the
fleets of these two types of fossil-fuel generating capacity. Figure 2-3 also includes the
distribution of generation, which is similar to the distribution of capacity.
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0 10 20 30 40 50 60 70
Age of EGU (years)
¦ Gas Cap — — —Gas Gen Coal Cap — — —Coal Gen
Figure 2-3 Cumulative Distribution in 2019 of Coal and Natural Gas Electricity
Capacity and Generation, by Age
Source: eGRID 2020 (January 2022 release from EPA eGRlD website). Figure presents data from generators that
came online between 1950 and 2020 (inclusive); a 71-year period. Full eGRID data includes generators that came
online as far back as 1915. Full data from 1915 onward is used in calculating cumulative distributions; figure
truncation at 70 years is merely to improve visibility of diagram.
The locations of existing fossil units in EPA's National Electric Energy Data System
(NEEDS) v.6 are shown in Figure 2-4.
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Figure 2-4 Fossil Fuel-Fired Electricity Generating Facilities, by Size
Source: National Electric Energy Data System (NEEDS) v.6
Note: Tliis map displays fossil capacity at facilities in the NEEDS v.6 database, wliich reflects generating capacity
expected to be on-line at the end of 2023. This includes planned new builds already under construction and planned
retirements. In areas with a dense concentration of facilities, some facilities may be obscured.
2.2.2 Transmission
Transmission is the term used to describe the bulk transfer of electricity over a network
of high voltage lines, from electric generators to substations where power is stepped down for
local distribution. In the U.S. and Canada, there are three separate interconnected networks of
high voltage transmission lines,11 each operating synchronously. Within each of these
transmission networks, there are multiple areas where the operation of power plants is monitored
and controlled by regional organizations to ensure that electricity generation and load are kept in
balance. In some areas, the operation of the transmission system is under the control of a single
11 These three network interconnections are the Western Interconnection, comprising the western parts of both the
U.S. and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection,
comprising the eastern parts of both the U.S. and Canada (except those part of eastern Canada that are in the Quebec
Interconnection), and the Texas Interconnection (which encompasses the portion of the Texas electricity system
commonly known as the Electric Reliability Council of Texas (ERCOT)). See map of all NERC interconnections at
https://www.nerc.com/AboutNERC/keyplayers/PublisliingImages/NERC%20Interconnections.pdf.
2-9
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regional operator;12 in others, individual utilities13 coordinate the operations of their generation,
transmission, and distribution systems to balance the system across their respective service
territories.
2.2.3 Distribution
Distribution of electricity involves networks of lower voltage lines and substations that
take the higher voltage power from the transmission system and step it down to lower voltage
levels to match the needs of customers. The transmission and distribution system is a classic
example of a natural monopoly, in part because it is not practical to have more than one set of
lines running from the electricity generating sources to substations or from substations to
residences and businesses.
Over the last few decades, several jurisdictions in the U.S. began restructuring the power
industry to separate transmission and distribution from generation, ownership, and operation.
Historically, vertically integrated utilities established much of the existing transmission
infrastructure. However, as parts of the country have restructured the industry, transmission
infrastructure has also been developed by transmission utilities, electric cooperatives, and
merchant transmission companies, among others. Distribution, also historically developed by
vertically integrated utilities, is now often managed by a number of utilities that purchase and
sell electricity, but do not generate it. As discussed below, electricity restructuring has focused
primarily on efforts to reorganize the industry to encourage competition in the generation
segment of the industry, including ensuring open access of generation to the transmission and
distribution services needed to deliver power to consumers. In many states, such efforts have also
included separating generation assets from transmission and distribution assets to form distinct
economic entities. Transmission and distribution remain price-regulated throughout the country
based on the cost of service.
12 For example, PMJ Interconnection, LLC, Western Area Power Administration (which comprises four sub-
regions).
13 For example, Los Angeles Department of Power and Water, Florida Power and Light.
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2.3 Sales, Expenses, and Prices
These electric generating sources provide electricity for ultimate commercial, industrial
and residential customers. Each of the three major ultimate categories consume roughly a quarter
to a third of the total electricity produced14 (see Table 2-4). Some of these uses are highly
variable, such as heating and air conditioning in residential and commercial buildings, while
others are relatively constant, such as industrial processes that operate 24 hours a day. The
distribution between the end use categories changed very little between 2015 and 2021.
Table 2-4 Total U.S. Electric Power Industry Retail Sales, 2015 and 2021 (billion kWh)
2015
2021
Sales/Direct Use
Share of Total
Sales/Direct Use
Share of Total
(Billion kWh)
End Use
(Billion kWh)
End Use
Residential
1,404
36%
1,470
37%
Commercial
1,361
35%
1,328
34%
Sales
Industrial
987
25%
1,001
25%
Transportation
8
0%
6
0%
Total
3,759
96%
3,806
96%
Direct Use
141
4%
139
Total End Use
3,900
100%
3,945
Source: Table 2.2, EIA Electric Power Annual, 2021
Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes net imported
electricity and loss of electricity that occurs through transmission and distribution, along with data collection frame
differences and non-sampling error. Direct Use represents commercial and industrial facility use of onsite net
electricity generation; electricity sales or transfers to adjacent or co-located facilities; and barter transactions.
2.3.1 Electricity Prices
Electricity prices vary substantially across the U.S., differing both between the ultimate
customer categories and by state and region of the country. Electricity prices are typically
highest for residential and commercial customers because of the relatively high costs of
distributing electricity to individual homes and commercial establishments. The higher prices for
residential and commercial customers are the result both of the necessary extensive distribution
network reaching to virtually every part of the country and every building, and also the fact that
generating stations are increasingly located relatively far from population centers (which
14 Transportation (primarily urban and regional electrical trains) is a fourth ultimate customer category which
accounts less than one percent of electricity consumption.
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increases transmission costs). Industrial customers generally pay the lowest average prices,
reflecting both their proximity to generating stations and the fact that industrial customers
receive electricity at higher voltages (which makes transmission more efficient and less
expensive). Industrial customers frequently pay variable prices for electricity, varying by the
season and time of day, while residential and commercial prices historically have been less
variable. Overall industrial customer prices are usually considerably closer to the wholesale
marginal cost of generating electricity than residential and commercial prices.
On a state-by-state basis, all retail electricity prices vary considerably. In 2021, the
national average retail electricity price (all sectors) was 11.10 cents/kWh, with a range from 8.17
cents (Idaho) to 30.31 cents (Hawaii).15
Average national retail electricity prices decreased between 2010 and 2021 by 8 percent
in real terms (2019 dollars), and 5 percent between 2015-21.16 The amount of decrease differed
for the three major end use categories (residential, commercial, and industrial). National average
industrial prices decreased the most (7 percent), and residential prices decreased the least (4
percent) between 2015-21. The real year prices for 2010 through 2021 are shown in Figure 2-5.
< 12
>
i
: io
r 8
> 6
r 4
I 2
j
]
0
Figure 2-5 Real National Average Electricity Prices (including taxes) for Three Major
End-Use Categories
15 EIA State Electricity Profiles with Data for 2021 (http://www.eia.gov/electricity/state/)
16 All prices in this section are estimated as real 2019 prices adjusted using the GDP implicit price deflator unless
otherwise indicated.
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Source: EIA. Electric Power Annual 2021, Table 2.4.
Most of these electricity price decreases occurred between 2014 and 2015, when nominal
residential electricity prices followed inflation trends, while nominal commercial and industrial
electricity prices declined. The years 2016 and 2017 saw an increase in nominal commercial and
industrial electricity prices, while 2018 and 2019 saw flattening of this growth. Industrial
electricity prices declined in 2019 and 2020 due to the effects of the pandemic. Prices rose in
2021 as a result of higher input fuel prices and increasing demand. The increase in nominal
electricity prices for the major end use categories, as well as increases in the gross domestic
product (GDP) price index for comparison, are shown in Figure 2-6.
25%
4
~
20%
-5%
Residential Commercial Industrial — — —GDP Price
Figure 2-6 Relative Increases in Nominal National Average Electricity Prices for Major
End-Use Categories (including taxes), With Inflation Indices
Source: EIA. Electric Power Annual 2021, Table 2.4.
2.3.2 Prices of Fossil Fuel Used for Generating Electricity
Another important factor in the changes in electricity prices are the changes in delivered
fuel prices17 for the three major fossil fuels used in electricity generation: coal, natural gas, and
petroleum products. Relative to real prices in 2014, the national average real price (in 2019
dollars) of coal delivered to EGUs in 2020 had decreased by 26 percent, while the real price of
17 Fuel prices in this section are all presented in terms of price per MMBtu to make the prices comparable.
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natural gas decreased by 56 percent. The real price of delivered petroleum products also
decreased by 55 percent, and petroleum products declined as an EGU fuel (in 2020 petroleum
products generated 1 percent of electricity). The combined real delivered price of all fossil fuels
(weighted by heat input) in 2020 decreased by 39 percent over 2014 prices. Figure 2-7 shows the
relative changes in real price of all 3 fossil fuels between 2010 and 2021.
-80%
Coal Petroleum Natural Gas
2016 2017 2018 2019 2020 2021
2010
-40%
Figure 2-7 Relative Real Prices of Fossil Fuels for Electricity Generation; Change in
National Average Real Price per MMBtu Delivered to EGU
Source: EIA. Electric Power Annual 2020 and 2021, Table 7.1.
2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2010 to 2021
An important aspect of the changes in electricity generation (i.e., electricity demand)
between 2010 and 2021 is that while total net generation increased by 1 percent over that period,
the demand growth for generation was lower than both the population growth (7 percent) and
real GDP growth (24 percent). Figure 2-8 shows the growth of electricity generation, population,
and real GDP during this period.
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25%
Real GDP Generation Population
Figure 2-8 Relative Growth of Electricity Generation, Population and Real GDP Since
2014
Sources: Generation: U.S. EIA Electric Power Annual 2021 and 2020. Population: U.S. Census. Real GDP: 2022
Economic Report of the President, Table B-3.
Because demand for electricity generation grew more slowly than both the population
and GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used
per person and per real dollar of output) during 2010 to 2021. On a per capita basis, real GDP per
capita grew by 16 percent between 2010 and 2021. At the same time electricity generation per
capita decreased by 6 percent. The combined effect of these two changes improved the overall
electricity generation efficiency in the U.S. market economy. Electricity generation per dollar of
real GDP decreased 19 percent. These relative changes are shown in Figure 2-9.
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25%
20%
-15% • ~~ -s.
-20%
-25%
Real GDP / Capita Generation / Capita ¦ Generation / Real GDP
Figure 2-9 Relative Change of Real GDP, Population and Electricity Generation
Intensity Since 2010
Sources: Generation: U.S. EIA Electric Power Annual 2021 and 2020. Population: U.S. Census. Real GDP: 2022
Economic Report of the President, Table B-3.
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3 COSTS, EMISSIONS, AND ENERGY IMPACTS
3.1 Introduction
This section presents the compliance cost, emissions, and energy impact analysis
performed for the MATS RTR. EPA used the Integrated Planning Model (IPM), developed by
ICF Consulting, to conduct its analysis. IPM is a dynamic linear programming model that can be
used to examine air pollution control policies for SO2, NOx, mercury, HC1, PM, and other air
pollutants throughout the U.S. for the entire power system. Documentation for EPA's Power
Sector Modeling Platform v6 Using the Integrated Planning Model Post-IRA 2022 Reference
Case (hereafter IPM Documentation) can be found at https://www.epa.gov/airmarkets/power-
sector-modeling, and is available in the docket for this action.
3.2 EPA's Post-IRA 2022 Reference Case
IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be
used to project power sector behavior under future business-as-usual conditions and to examine
prospective air pollution control policies throughout the contiguous U.S. for the entire electric
power system. For this RIA, EPA used IPM to project likely future electricity market conditions
with and without this proposed rulemaking and a more stringent regulatory alternative.
IPM, developed by ICF, is a multi-regional, dynamic, deterministic linear programming
model of the contiguous U.S. electric power sector. It provides estimates of least cost capacity
expansion, electricity dispatch, and emissions control strategies while meeting energy demand
and environmental, transmission, dispatch, and reliability constraints. IPM's least-cost dispatch
solution is designed to ensure generation resource adequacy, either by using existing resources or
through the construction of new resources. IPM addresses reliable delivery of generation
resources for the delivery of electricity between the 78 IPM regions, based on current and
planned transmission capacity, by setting limits to the ability to transfer power between regions
using the bulk power transmission system. Notably, the model includes cost and performance
estimates for state-of-the-art air pollution control technologies with respect to mercury, filterable
PM, and other HAP controls.
EPA has used IPM for almost three decades to better understand power sector behavior
under future business-as-usual conditions and to evaluate the economic and emissions impacts of
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prospective environmental policies. The model is designed to reflect electricity markets as
accurately as possible. EPA uses the best available information from utilities, industry experts,
gas and coal market experts, financial institutions, and government statistics as the basis for the
detailed power sector modeling in IPM. The model documentation provides additional
information on the assumptions discussed here as well as all other model assumptions and
inputs.18
The model incorporates a detailed representation of the fossil-fuel supply system that is
used to estimate equilibrium fuel prices. The model uses natural gas fuel supply curves and
regional gas delivery costs (basis differentials) to simulate the fuel price associated with a given
level of gas consumption within the system. These inputs are derived using ICF's Gas Market
Model (GMM), a supply/demand equilibrium model of the North American gas market.19
IPM also endogenously models the partial equilibrium of coal supply and EGU coal
demand levels throughout the contiguous U.S., taking into account assumed non-power sector
demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal
transport network, which consists of over four thousand linkages representing rail, barge, and
truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough
bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that
power plants would face if selecting that coal over the modeling time horizon. The IPM
documentation outlines the methods and data used to quantify the economically recoverable coal
reserves, characterize their cost, and build the 36 coal regions' supply curves.20
To estimate the annualized costs of additional capital investments in the power sector,
EPA uses a conventional and widely accepted approach that applies a capital recovery factor
(CRF) multiplier to capital investments and adds that to the annual incremental operating
expenses. The CRF is derived from estimates of the power sector's cost of capital (i.e., private
discount rate), the amount of insurance coverage required, local property taxes, and the life of
18 Detailed information and documentation of EPA's Baseline run using EPA's Post-IRA IPM 2022 Reference Case,
including all the underlying assumptions, data sources, and architecture parameters can be found on EPA's website
at: https://www.epa.gov/airmarkets/power-sector-modeling.
19 See Chapter 8 of EPA's Post-IRA IPM 2022 Reference Case Documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling.
20 See Chapter 7 EPA's Post-IRA IPM 2022 Reference Case Documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling.
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capital.21 It is important to note that there is no single CRF factor applied in the model; rather, the
CRF varies across technologies, book life of the capital investments, and regions in the model in
order to better simulate power sector decision-making.
EPA has used IPM extensively over the past three decades to analyze options for
reducing power sector emissions. Previously, the model has been used to estimate the costs,
emission changes, and power sector impacts in the RIAs for the Clean Air Interstate Rule (U.S.
EPA, 2005), the Cross-State Air Pollution Rule (U.S. EPA, 201 la), the Mercury and Air Toxics
Standards (U.S. EPA, 201 lb), the Clean Power Plan for Existing Power Plants (U.S. EPA,
2015b), the Cross-State Air Pollution Update Rule (U.S. EPA, 2016), the Repeal of the Clean
Power Plan, and the Emission Guidelines for Greenhouse Gas Emissions from Existing Electric
Utility Generating Units (U.S. EPA, 2019), and the Revised Cross-State Air Pollution Update
Rule (U.S. EPA, 2021).
EPA has also used IPM to estimate the air pollution reductions and power sector impacts
of water and waste regulations affecting EGUs, including contributing to RIAs for the Cooling
Water Intakes (316(b)) Rule (U.S. EPA, 2014), the Disposal of Coal Combustion Residuals from
Electric Utilities rule (U.S. EPA, 2015c), the Steam Electric Effluent Limitation Guidelines (U.S.
EPA, 2015a), and the Steam Electric Reconsideration Rule (U.S. EPA, 2020).
The model and EPA's input assumptions undergo periodic formal peer review. The
rulemaking process also provides opportunity for expert review and comment by a variety of
stakeholders, including owners and operators of capacity in the electricity sector that is
represented by the model, public interest groups, and other developers of U.S. electricity sector
models. The feedback that the Agency receives provides a highly detailed review of key input
assumptions, model representation, and modeling results. IPM has received extensive review by
energy and environmental modeling experts in a variety of contexts. For example, in October
2014 U.S. EPA commissioned a peer review22 of EPA Baseline run version 5.13 using IPM.
Additionally, and in the late 1990s, the Science Advisory Board reviewed IPM as part of the
21 See Chapter 10 of
EPA's Post-IRA IPM 2022 Reference Case Documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling.
22 See Response and Peer Review Report EPA Baseline run Version 5.13 Using IPM, available at:
https://www.epa.gov/airmarkets/response-and-peer-review-report-epa-base-case-version-513-using-ipm.
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CAA Amendments Section 812 prospective studies23 that are periodically conducted. The
Agency has also used the model in a number of comparative modeling exercises sponsored by
Stanford University's Energy Modeling Forum over the past 20 years. IPM has also been
employed by states (e.g., for the Regional Greenhouse Gas Initiative, the Western Regional Air
Partnership, Ozone Transport Assessment Group), other Federal and state agencies,
environmental groups, and industry.
3.3 Baseline
The modeled "baseline " for any regulatory impact analysis is a business-as-usual
scenario that represents expected behavior in the electricity sector under market and regulatory
conditions in the absence of a regulatory action. As such, the baseline run represents an element
of the baseline for this RIA.24 EPA frequently updates the baseline modeling to reflect the latest
available electricity demand forecasts from the U.S. EIA as well as expected costs and
availability of new and existing generating resources, fuels, emission control technologies, and
regulatory requirements.
For our analysis of the proposed MATS RTR rule, EPA used EPA's Post-IRA 2022
Reference Case to provide power sector emissions projections for air quality modeling, as well
as a companion updated database of EGU units (the National Electricity Energy Data System or
NEEDS v621 rev: 10-14-2225) that is used in EPA's modeling applications of IPM. The baseline
for this proposal includes the proposed GNP, the Revised CSAPR Update, CSAPR Update, and
CSAPR, as well as MATS. The Baseline run also includes the 2015 Effluent Limitation
Guidelines (ELG) and the 2015 Coal Combustion Residuals (CCR), and the recently finalized
2020 ELG and CCR rules.26
This version of the model, which is used as the baseline for this RIA, also includes recent
updates to state and federal legislation affecting the power sector, including Public Law 117-169,
23 http://www2.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act
24 As described in Chapter 5 of EPA's Guidelines for Preparing Economic Analyses, the baseline "should
incorporate assumptions about exogenous changes in the economy that may affect relevant benefits and costs (e.g.,
changes in demographics, economic activity, consumer preferences, and technology), industry compliance rates,
other regulations promulgated by EPA or other government entities, and behavioral responses to the proposed rule
by firms and the public." (USEPA, 2010).
25 https://www.epa.gov/power-sector-modeling/national-electric-energy-data-system-needs-v6
26 For a full list of modeled policy parameters, please see:
https://www.epa.gov/airmarkets/power-sector-modeling.
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136 Stat. 1818 (August 16, 2022), commonly known as the Inflation Reduction Act of 2022 (the
IRA). The IPM Documentation includes a summary of all legislation reflected in this version of
the model as well as a description of how that legislation is implemented in the model.
The inclusion of the proposed GNP and other regulatory actions (including federal, state,
and local actions) in the base case is necessary in order to reflect the level of controls that are
likely to be in place in response to other requirements apart from the scenarios analyzed in this
section. As the GNP was finalized on March 15, 2023, any differences between the proposal and
final GNP will not be reflected in the baseline for this proposal. This base case will provide
meaningful projections of how the power sector will respond to the cumulative regulatory
requirements for air emissions in totality, while isolating the incremental impacts of MATS RTR
relative to a base case with other air emission reduction requirements separate from this proposed
action.
The analysis of power sector cost and impacts presented in this section is based on a
single baseline run, and represents incremental impacts projected solely as a result of compliance
with the proposed MATS RTR or the analyzed alternatives
3.4 Regulatory Options Analyzed
For this RIA, EPA analyzed the three regulatory options summarized in the table below,
which are described in more detail in Section 1.3.1. The remainder of this section discusses the
approach used for estimating the costs and/or emissions impacts of each provision of the
proposed rule.
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Table 3-1 Summary of Proposed Regulatory Options Examined in this RIA
Regulatory Options Examined in this RIA
Provision
Less Stringent
Proposed
More Stringent
Filterable PM
Standard
(Surrogate
Standard for Non-
Retain existing filterable
PM standard of 0.030
lb/MMBtu
revised filterable PM
standard of 0.010
lb/MMBtu
revised filterable PM
standard of 0.006
lb/MMBtu
Mercury Standard
Retain mercury standard
for lignite-fired EGUs of
4.0 lb/TBtu
revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
revised mercury standard
for lignite-fired EGUs of
1.2 lb/TBtu
Continuous
Emissions
Monitoring Systems
(PM CEMS)
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Require installation of PM
CEMS to demonstrate
compliance
Startup definition
Remove startup definition
#2
Remove startup definition
#2
Remove startup definition
#2
The revisions to the filterable PM standard and the mercury standard are modeled
endogenously within IPM. For the filterable PM standard, emissions controls and associated
costs are modeled based on information available in the memorandum titled: "2023 Technology
Review for the Coal- and Oil-Fired EGU Source Category" which is available in the docket. This
memorandum summarizes the filterable PM emissions rate for each existing EGU. Based on the
emissions rates detailed in this memorandum, EPA assumed various levels of ESP upgrades,
upgrades to existing fabric filters, or new fabric filter installations to comply with each of the
proposed standards in the modeling. Those assumptions are detailed in Table 3-2.
Table 3-2 PM Control Technology Modeling Assumptions
PM
Control Strategy
Capital Cost
Filterable
PM Reduction
Minor
ESP Upgrades
$16.5/kW
7.5%
Typical
ESP Upgrades
$55/kW
15%
ESP Rebuild
$88/kW
40%
(0.0051b/MMBtu floor)
Upgrade Existing FF Bags
Unit-specific, approximately $15K -
$500K annual O&M
50%
(0.002 lb/MMBtu floor)
New Fabric Filter
(6.0 A/C Ratio)
Unit-specific,
$150-360/kW*
90%
(0.002 lb/MMBtu floor)
* https://www.epa.gov/system/files/documents/2021 -09/attachment_5-
7_pm_control_cost_development_methodology.pdf
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The cost and reductions associated with control of mercury emissions at lignite-fired EGUs are
also modeled endogenously and reflect the assumption that each of these EGUs replace standard
powdered activated carbon (PAC) sorbent with halogenated PAC sorbent.
While more detail on the costs associated with the proposal to require PM CEMS and the
proposed change in the startup definition is presented in Section 3.5.2, we note here that these
costs were estimated exogenously without the use of the model that provides the bulk of the cost
analysis for this RIA. As a result, the results of the power sector modeling do not include costs
associated with these provisions, but the costs associated with requiring PM CEMS and the
change in the startup definition are included in the total cost projections for the rule for each of
the regulatory options analyzed in this RIA. As the incremental costs of requiring PM CEMS is
negative and small relative to other aspects of this proposed rulemaking, we do not think the
endogenous incorporation of these costs would change any projected results in a meaningful
way.
3.5 Power Sector Impacts
3.5.1 Emissions
As indicated previously, this RIA presents emissions reductions estimates in years 2028,
2030, and 2035 based on IPM projections. Table 3-3 presents the estimated impact on reduction
in power sector emissions resulting from compliance with the evaluated regulatory control
alternatives (i.e., filterable PM and mercury standards) in the contiguous U.S. Note the less
stringent regulatory alternative in this RIA was not modeled using IPM. As a result, power sector
impacts are not estimated for the less stringent regulatory option, but the costs associated with
requiring PM CEMS are included in all options. The projections indicate that both the proposed
rule and the more stringent alternative result in emissions reductions in all run years, and those
emission reductions follow an expected pattern: the proposed rule, which revises the filterable
PM standard to 0.010 lb/MMBtu, produces smaller emissions reductions than the more stringent
alternative, which revises the filterable PM standard to 0.006 lb/MMBtu. The additional
reductions of mercury emissions in the more stringent alternative result from the additional coal
steam retirements in this scenario. Note the less stringent regulatory alternative has no quantified
emissions reductions associated with the proposed requirements for PM CEMS and the removal
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of startup definition number two. As a result, there are no quantified benefits associated with this
regulatory option.
Table 3-3 EGU Emissions and Emissions Changes for the Baseline Run and the
Proposed Rule and More Stringent Alternatives for 2028, 2030, and 2035 a
Total Emissions Change from Baseline
Year
Baseline
Run
Proposed
Rule
More-
Stringent
Alternative
Proposed
Rule
More-
Stringent
Alternative
2028
5,019
4,957
4,811
-62
-208
Mercury (lbs.)
2030
4,206
4,139
4,037
-67
-169
2035
3,219
3,137
3,052
-82
-168
PM2.5 (thousand
tons)
2028
2030
74.6
65.5
74.2
65.1
72
64
-0.4
-0.4
-2.6
-1.5
2035
46.6
45.8
45.3
-0.8
-1.3
2028
394
393
382
-0.9
-11.6
SO2 (thousand tons)
2030
282
282
282
-0.5
-0.3
2035
130
128
121
-1.5
-8.8
Ozone-season NOx
(thousand tons)
2028
2030
2035
195
163
104
195
163
101
188
158
99
-0.2
-0.4
-3.2
-7.2
-5.1
-5.6
Annual NOx
(thousand tons)
2028
2030
2035
457
368
214
456
367
211
439
358
205
-0.4
-0.8
-3.4
-18.1
-9.5
-8.7
2028
2.6
2.6
2.5
0.0
-0.2
HC1 (thousand tons)
2030
1.8
1.8
1.7
0.0
-0.1
2035
0.9
0.9
0.8
0.0
-0.1
CO2 (million
metric tons)
2028
2030
2035
1222
972
608
1222
971
604
1200
963
605
-0.2
-0.8
-4.6
-21.9
-8.7
-2.9
a This analysis is limited to the geographically contiguous lower 48 states.
3.5.2 Compliance Costs
3.5.2.1 Power Sector Costs
The power industry's "compliance costs" are represented in this analysis as the change in
electric power generation costs between the baseline and policy scenarios and are presented in
Table 3-4. In simple terms, these costs are an estimate of the increased power industry
expenditures required to implement the proposed requirements. The total compliance costs,
presented in section 3.5.2.4, are estimated for this RIA as the sum of two components. The first
3-8
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component, estimated using the modeling discussed above, is presented below in Table 3-4.
This component constitutes the majority of the incremental costs for the proposal and more
stringent option. The second component, the costs of the proposed PM CEMS requirement, is
discussed in section 3.5.2.2.
EPA projects that the annual incremental compliance cost of the proposed rule is $62
million, $52 million, and $45 million (2019 dollars) annually in 2028, 2030, and 2035,
respectively. The annual incremental cost is the projected additional cost of complying with the
proposed rule in the year analyzed and includes the amortized cost of capital investment and any
applicable costs of operating additional pollution controls, investments in new generating
sources, shifts between or amongst various fuels, and other actions associated with compliance.
This projected cost does not include the compliance calculated outside of IPM modeling, namely
the compliance costs related to PM CEMS. See Section 3.5.2.2 for further details on these costs.
EPA believes that the cost assumptions used for this RIA reflect, as closely as possible, the best
information available to the Agency today.
Table 3-4 National Power Sector Compliance Cost Estimates (millions of 2019 dollars)
for the Proposed Rule and More Stringent Alternative for 2028, 2030, and 2035
Analysis Year
Proposed Rule
More Stringent Alternative
2028 (Annualized)
62
928
2030 (Annualized)
52
1,061
2035 (Annualized)
45
290
Note: The less stringent regulatory alternative in this RIA was not modeled using IPM. As a result power sector
impacts are not estimated for the less stringent regulatory option, but the costs associated with requiring PM CEMS
are included in the total cost across regulatory options.
Additionally, EPA projects that the annual incremental compliance cost of the more
stringent alternative is $928 million, $1 billion, and $290 million (2019 dollars) annually in
2028, 2030, and 2035, respectively. Relative to the proposed rule, these costs are notably higher.
The difference in projected compliance costs results from EPA's assumption that more costly
controls would be installed to comply with the lower filterable PM emissions limit. A small
percentage of the total compliance costs for the more stringent alternative is attributable to the
capital and operating costs of these additional controls, and the vast majority of the incremental
cost is associated with the projected changes in operating capacity which decrease significantly
by 2035 (e.g., construction of new capacity). See Section 3.5.4 for a discussion of projected
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capacity changes and Section 3.6 for a discussion of the uncertainty regarding necessary
pollution controls.
3.5.2.2 PMCEMS Costs
In addition to revising the PM emission standard for existing coal-fired EGUs, EPA is
proposing a revision to the requirements for demonstrating compliance with the PM emission
standard for coal-fired EGUs. Either of the two filterable PM standards under consideration
would render the current limit for the LEE program moot, since they would be two-thirds and
two-fifths, respectively, of the current PM LEE limit. Therefore, EPA proposes to remove PM
from the LEE program. Currently, EGUs that are not LEE units can demonstrate compliance
with the filterable PM standard either by conducting performance testing quarterly, use of PM
continuous parameter monitoring systems (CPMS) or using PM CEMS.
After considering updated information on the costs for performance testing compared to
the cost of PM CEMS and capabilities of PM CEMS measurement abilities, as well as the
benefits of using PM CEMS, which include increased transparency and accelerated identification
of anomalous emissions, EPA is proposing to require that all MATS-affected EGUs demonstrate
compliance with the PM emission standard by using PM CEMS.
The revision of PM limits in the proposal and more stringent alternative alters the
composition and duration of testing runs in facilities that use either performance testing
methodology. For units currently employing M5 quarterly testing, four tests would be required at
an individual cost of $15,522 and an annual cost of $62,088.27 EPA calibrated its cost estimates
for PM CEMS in response to observed installations, manufacturer input, and engineering
analyses. These calibrations include an assumed replacement lifespan of 15 years and an interest
rate of 7 percent to approximate the prevailing bank prime rate. For the portion of EGUs that
employ PM CEMS, manufacturer input leads to an annualized cost of $32,559, which is slightly
lower than the current cost of $33,643 for firms utilizing PM CEMS. All installations of PM
CEMS currently in place took place in between 2012 and 2015. With a 15-year expected useful
life, the assumption is made that all units would require initial installation of new PM CEMS,
including those that already utilize the technology.
27 EGUs receiving contractual or quantity discounts from performance test provides may incur lower costs.
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To produce an inventory of total units which would require the installation of PM CEMS
in the proposal and more stringent alternative as well as their initial characterization for
juxtaposition of current and proposal costs, EPA began with an inventory of all existing coal-
fired EGUs with capacity great enough to be regulated by MATS. That inventory was then
filtered to remove EGUs with planned retirements prior to 2028 from analysis of both the
baseline and proposal. Within that remaining inventory of 358 EGUs, 126 units are assumed to
have installed PM CEMS between 2012 and 2015, while the remaining 232 units are assumed to
use quarterly testing and not have existing PM CEMS installations.
Table 3-5 Costs of Proposed Continuous Emissions Monitoring (PM CEMS)
Requirement
Baseline
Total
Proposed
Compliance
Cost (per
Baseline
Rule (per
Proposed
Incremental
Approach in
Units
year per
Costs (per
year per
Rule Costs
Costs (per
Baseline
(no.)
unit)
year)
unit)
(per year)
year)
Quarterly Testing
200
$62,000
$12,000,000
$33,000
$6,300,000
-$5,800,000
PM CEMS
110
$34,000
$3,600,000
$33,000
$3,500,000
-$120,000
Total
300
—
$16,000,000
—
$9,800,000
-$5,900,000
Note: Values rounded to two significant figures
As detailed in Table 3-5, relative to the baseline scenario, revised PM CEMS cost estimates in
the proposal lead to a reduction of costs of $1,000 year per unit and about $120,000 per year in
total. For EGUs currently employing quarterly testing, the proposal results in cost reductions of
$29,000 per year per unit and $5.8 million per year in total. The estimated aggregate sector
impact thus sums to a cost reduction of about $5.9 million per year.
3.5.2.3 Startup Definition Costs
EPA is proposing to remove one of the two options for defining the startup period for
EGUs. The first option defines startup as either the first-ever firing of fuel in a boiler for the
purpose of producing electricity, or the firing of fuel in a boiler after a shutdown event for any
purpose. Startup ends when any of the steam from the boiler is used to generate electricity for
sale over the grid or for any other purpose (including on-site use). In the second option, startup is
defined as the period in which operation of an EGU is initiated for any purpose. Startup begins
with either the firing of any fuel in an EGU for the purpose of producing electricity or useful
thermal energy (such as heat or steam) for industrial, commercial, heating, or cooling purposes
(other than the first-ever firing of fuel in a boiler following construction of the boiler) or for any
3-11
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other purpose after a shutdown event. Startup ends four hours after the EGU generates electricity
that is sold or used for any other purpose (including on-site use), or four hours after the EGU
makes useful thermal energy (such as heat or steam) for industrial, commercial, heating, or
cooling purposes, whichever is earlier. This second option, referred to as paragraph (2) of the
definition of "startup," required clean fuel use to the maximum extent possible, operation of PM
control devices within one hour of introduction of primary fuel (i.e., coal, residual oil, or solid
oil-derived fuel) to the EGU, collection and submission of records of clean fuel use and
emissions control device capabilities and operation, as well as adherence to applicable numerical
standards within four hours of the generation of electricity or thermal energy for use either on
site or for sale over the grid (i.e., the end of startup) and to continue to maximize clean fuel use
throughout that period.
According to EPA analysis, the owners or operators of at least 98 percent of all other
coal- and oil-fired EGUs have made the requisite adjustments, whether through greater clean fuel
capacity, better tuned equipment, better trained staff, a more efficient or better design structure,
or a combination of factors, to be able to meet the requirements of paragraph (1) of the definition
of "startup." As demonstrated by the vast majority of EGUs currently relying on the work
practice standards in paragraph (1) of the definition of "startup," we believe such a change is
achievable by all EGUs; further, we expect such a change would result in little to no additional
expenditure since the additional recordkeeping and reporting provisions associated with the work
practice standards of paragraph (2) of the definition of "startup" were more expensive than the
requirements of paragraph (1) of the definition of "startup." As a result, this RIA does not
incorporate any additional costs as a result of this proposed provision.
3.5.2.4 Total Compliance Costs
The estimates of the total compliance costs are presented in Table 3-6. The total costs are
composed of the change in electric power generation costs between the baseline and policy
scenarios as presented in Table 3-4 and the incremental cost of the proposed PM CEMS
requirement as detailed in Table 3-5.
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Table 3-6 Stream of Projected Compliance Costs across Proposed Rule and Less and
More Stringent Regulatory Alternatives (millions of 2019 dollars) a
Regulatory Alternative
Year
Proposed Rule
Less Stringentb
More Stringent
2028b
56
-5.9
920
2030b (mapped to 2029 to 2031)
46
-5.9
1,100
2035b (mapped to 2032 to 2037)
39
-5.9
280
3% Discount Rate
Present Value (PV)
330
-45
4,600
Equivalent Annualized Value (EAV)
38
-5.2
540
7% Discount Rate
Present Value (PV)
230
-31
3,400
Equivalent Annualized Value (EV)
33
-4.5
490
a Positive values indicate costs, and negative values indicate cost savings in this table. Values rounded to two
significant figures
b IPM analysis years mapped to individual calendar years in order to calculate PV and EAVs. Values rounded to
two significant figures
3.5.3 Projected Compliance Actions for Emissions Reductions
Electric generating units subject to the mercury and filterable PM emission limits in this
proposed rule will likely use various mercury and PM control strategies to comply. This section
summarizes the projected compliance actions related to each of these emissions limits.
The 2028 baseline includes approximately 7 GW of operational minemouth EGU
capacity designed to burn low rank virgin coal. All of this capacity is currently equipped with
Activated Carbon Injection (ACI) technology, and operation of this technology is reflected in the
baseline. In the proposed and more stringent modeling scenarios, each of these EGUs projected
to consume lignite is assigned an additional variable operating cost that is consistent with
achieving a 1.2 lb/MMBtu limit. In the proposed option, this additional cost does not result in
incremental retirements for these units, nor does it result in a significant change to the projected
generation level for these units.
The baseline also includes 4.8 GW of operational coal capacity that, based on the analysis
documented in the EPA docketed memorandum titled: "2023 Technology Review for the Coal-
and Oil-Fired EGU Source Category," EPA assumes would either need to improve existing PM
controls or install new PM controls to comply with the proposed option in 2028. The various PM
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control upgrades that EPA assumes would be necessary to achieve with the emissions limits
analyzed are summarized in Table 3-7.
Table 3-7 Affected Capacity Operational in the Baseline by PM Control Strategy for
the Proposed Rule and More Stringent Alternative in 2028 (GW)
Proposed Rule
More Stringent Alternative
PM Control Strategy
Affected
Capacity
Operational in
Baseline
Projected
Retrofits in
Proposed Rule
Affected
Capacity
Operational in
Baseline
Projected
Retrofits in
More-Stringent
Alternative
Minor ESP upgrades
1.1
1.1
--
--
Typical ESP Upgrades
0.5
0
--
--
ESP Rebuild
0.4
0.4
--
--
FF Bag Upgrade
1.2
1.2
7.6
7.6
New Fabric Filter
1.5
1.5
15.0
3.6
Total
4.8
4.3
22.7
11.3
The vast majority of the 4.8 GW that EPA assumes would need to take some compliance
action to meet the proposed standards is currently operating existing ESPs and/or fabric filters.
Nearly all of that capacity is projected to install the controls summarized in Table 3-7 and remain
operational in 2028, and about 500 MW of that coal steam capacity is projected to retire in
response to the proposed rule.
Under the more stringent alternative, EPA assumes that 22.7 GW of capacity that is
projected to be operational in the baseline would need to take some compliance action in order to
meet the proposed standards. About half of that capacity (about 11.3 GW) is projected to remain
operational with the installation of those controls in 2028.
3.5.4 Generating Capacity
In this section, we discuss the projected changes in capacity by fuel type, building on and
adding greater context to the information presented in the previous section. We first look at total
capacity by fuel type, then retirements by fuel type, and finally new capacity builds by fuel type
for the 2028, 2030, and 2035 run years.
Table 3-8 shows the total net projected capacity by fuel type for the baseline run and
regulatory control alternatives for 2028, 2030, and 2035. Here, we see the net effects of projected
retirements (Table 3-9) and new capacity builds (see Table 3-10). All incremental changes in
capacity projected to result in response to the proposed rule for any given fuel type are one
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percent or less, and all under 1 GW. The more stringent alternative, on the other hand, is
projected to result in a fleet consisting of slightly more operational natural gas capacity by 2035,
and slightly less operational coal capacity.
Table 3-8 2028, 2030, and 2035 Projected U.S. Capacity by Fuel Type for the Baseline
and the Proposed Rule and More Stringent Alternative
Total Generation Capacity (GW)
Incremental Change from Baseline
Baseline
Proposed
Rule
More
Stringent
Proposed Rule
More Stringent
Alternative
Alternative
GW
%
GW
%
2028
Coal
100.5
99.9
88.2
-0.5
-0.5%
-12.2
-12.2%
Natural Gas
463.0
463.5
467.0
0.5
0.1%
4.0
0.9%
Oil/Gas Steam
62.8
62.7
62.8
-0.1
-0.1%
0.1
0.1%
Non-Hydro RE
314.8
314.6
316.5
-0.1
0.0%
1.8
0.6%
Hydro
102.1
102.1
102.1
0.0
0.0%
0.0
0.0%
Energy Storage
50.0
50.3
56.0
0.3
0.5%
6.0
11.9%
Nuclear
95.7
95.7
95.7
0.0
0.0%
0.0
0.0%
Other
6.9
6.9
6.9
0.0
0.0%
0.0
0.0%
Total
1,195.8
1,195.9
1,195.4
0.0
0.0%
-0.5
0.0%
2030
Coal
68.9
68.4
63.5
-0.6
-0.8%
-5.5
-8.0%
Natural Gas
461.1
461.5
465.2
0.5
0.1%
4.1
0.9%
Oil/Gas Steam
60.4
60.3
60.1
-0.1
-0.1%
-0.3
-0.5%
Non-Hydro RE
403.4
403.3
405.1
-0.1
0.0%
1.7
0.4%
Hydro
103.6
103.6
103.6
0.0
0.0%
0.0
0.0%
Energy Storage
68.1
68.2
69.0
0.1
0.2%
1.0
1.4%
Nuclear
91.9
91.9
91.9
0.0
0.0%
0.0
0.0%
Other
6.9
6.9
6.9
0.0
0.0%
0.0
0.0%
Total
1,264.3
1,264.2
1,265.2
-0.1
0.0%
0.9
0.1%
2035
Coal
44.0
43.7
39.3
-0.3
-0.8%
-4.7
-10.8%
Natural Gas
470.0
470.2
474.6
0.3
0.1%
4.6
1.0%
Oil/Gas Steam
59.2
59.1
59.2
-0.1
-0.1%
0.0
0.0%
Non-Hydro RE
667.6
668.4
667.6
0.8
0.1%
0.0
0.0%
Hydro
107.9
107.9
107.9
0.0
0.0%
0.0
0.0%
Energy Storage
98.2
98.3
98.4
0.1
0.1%
0.2
0.2%
Nuclear
83.6
83.6
83.6
0.0
0.0%
0.0
0.0%
Other
6.9
6.9
6.9
0.0
0.0%
0.0
0.0%
Total
1,537.4
1,538.2
1,537.5
0.8
0.0%
0.1
0.0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
Table 3-9 shows the total capacity projected to retire by fuel type for the baseline run and
the regulatory control alternatives in all run years. The incremental changes projected to occur in
response to the proposed rule are very small. The proposed rule is projected to result in an
3-15
-------
additional 500 MW of retired coal capacity (less than one percent). The more stringent
alternative is projected to result in additional incremental retirement of coal capacity: 12.2 GW
of incremental coal retirements in 2028, decreasing to 5.4 GW of incremental coal retirements in
2035. This decrease over time reflects an acceleration of projected retirements (some capacity
that was projected to retire in the 2035 baseline is projected to retire a few years earlier in the
more stringent policy scenario). In all scenarios analyzed, the model's least-cost dispatch
solution is designed to ensure generation resource adequacy, either by using existing resources or
through the construction of new resources.
Table 3-9 2028, 2030, and 2035 Projected U.S. Retirements by Fuel Type for the
Baseline Run and the Proposed Rule and More Stringent A
ternative
Retirements (GW)
Percent Change from Baseline
Proposed
More Stringent
Proposed
More Stringent
Baseline Rule
Alternative
Rule
Alternative
2028
Coal 56.5 57.0 68.7
Natural Gas 1.7 1.7 1.7
Oil/Gas Steam 8.4 8.5 8.4
Non-Hydro RE 3.0 3.0 2.9
Hydro 0.0 0.0 0.0
Nuclear 0.0 0.0 0.0
Other 0.0 0.0 0.0
Total 69J) 702 81.7
2030
Coal 82.0 82.5 87.9
Natural Gas 2.4 2.4 2.4
Oil/Gas Steam 12.4 12.4 12.7
Non-Hydro RE 3.3 3.3 3.3
Hydro 0.0 0.0 0.0
Nuclear 2.7 2.7 2.7
Other 0.1 0.1 0.1
Total 102.8 103.5 109.1
2035
Coal
105.0
105.4
110.4
0.3%
5.1%
Natural Gas
6.2
6.2
6.2
-0.1%
0.2%
Oil/Gas Steam
14.8
14.9
14.8
0.4%
-0.1%
Non-Hydro RE
3.3
3.3
3.3
0.0%
0.0%
Hydro
0.0
0.0
0.0
0%
0%
Nuclear
9.9
9.9
9.9
0.0%
0.0%
Other
0.1
0.1
0.1
0.0%
0.0%
Total
139.3
139.7
144.7
0.3%
3.9%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
0.9%
0.0%
0.7%
0.0%
0%
0%
0%
0.8%
21.6%
0.0%
-0.7%
-3.0%
0%
0%
0%
17.3%
0.7%
0.0%
0.5%
0.0%
0%
0.0%
0.0%
0.6%
7.3%
0.5%
2.7%
0.0%
0%
0.0%
0.0%
6.1%
3-16
-------
Finally, Table 3-10 shows the projected U.S. new capacity builds by fuel type for the
baseline run and the regulatory control alternatives in all run years. For the proposed rule, the
incremental changes in projected new capacity for any given fuel type are one percent or less,
and all under 1 GW. The more-stringent alternative is projected to result in an increase in
incremental builds in the energy storage (6.0 GW), natural gas (3.9 GW), and renewables (1.7
GW) categories in 2028. Some of these incremental changes reflect a projected acceleration of
new capacity that was projected to occur after 2028 in the baseline.
Table 3-10 2028, 2030, and 2035 Projected U.S. New Capacity Builds by Fuel Type for
the Baseline Run and the Proposed Rule and More Stringent Alternative
New Capacity (GW)
Percent Change from Baseline
Baseline
Proposed
Rule
More Stringent
Alternative
Proposed Rule
More Stringent
Alternative
2028
Coal
0.0
0.0
0.0
0.0%
0.0%
Natural Gas
31.6
32.0
35.5
1.4%
12.5%
Energy Storage
32.5
32.8
38.5
0.8%
18.3%
Non-Hydro RE
42.0
41.9
43.7
-0.3%
3.9%
Hydro
0.0
0.0
0.0
0.0%
0.0%
Nuclear
0.0
0.0
0.0
0.0%
0.0%
Other
0.0
0.0
0.0
0.0%
0.0%
Total
106.2
106.8
117.8
0.5%
10.9%
2030
Coal
0.0
0.0
0.0
0.0%
0.0%
Natural Gas
31.6
32.0
35.7
1.5%
13.0%
Energy Storage
50.6
50.7
51.5
0.3%
1.9%
Non-Hydro RE
130.8
130.7
132.5
-0.1%
1.3%
Hydro
1.5
1.5
1.5
0.0%
0.0%
Nuclear
0.0
0.0
0.0
0.0%
0.0%
Other
0.0
0.0
0.0
0.0%
0.0%
Total
214.5
215.0
221.2
0.2%
3.1%
2035
Coal
0.0
0.0
0.0
0.0%
0.0%
Natural Gas
45.0
45.2
49.6
0.5%
10.3%
Energy Storage
80.7
80.8
81.0
0.1%
0.3%
Non-Hydro RE
395.0
395.9
395.0
0.2%
0.0%
Hydro
5.8
5.8
5.8
0.0%
0.0%
Nuclear
0.0
0.0
0.0
0.0%
0.0%
Other
0.0
0.0
0.0
0.0%
0.0%
Total
526.5
527.7
531.3
0.2%
0.9%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
3-17
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3.5.5 Generation Mix
In this section, we discuss the projected changes in generation mix for the 2028, 2030,
and 2035 for the proposed rule and more stringent alternative. Table 3-11 presents the projected
generation and percentage changes in national generation mix by fuel type for run years 2028,
2030, and 2035. These generation mix estimates reflect a very modest increase in natural gas and
renewables and decrease in coal beginning in 2028 as a result of proposed rule and more
stringent alternative. Estimated changes in coal and natural gas use as a result of each regulatory
option are examined further in section 3.5.6
3-18
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Table 3-11 2028, 2030, and 2035 Projected U.S. Generation by Fuel Type for the
Baseline Run and the Proposed Rule and More Stringent Alternative
Generation Mix (TWh)
Incremental Change from Baseline
_ , More
Base Case ™pose Stringent
u e Alternative
^'ruuT' More Stringent Alternative
TWh % TWh %
2028
Coal
Natural Gas
Oil/Gas Steam
Non-Hydro RE
Hydro
Energy Storage
Nuclear
Other
Total
2030
Coal
Natural Gas
Oil/Gas Steam
Non-Hydro RE
Hydro
Energy Storage
Nuclear
Other
Total
2035
Coal
120
115
104
-4.1
-3.5%
-15.3
-12.8%
Natural Gas
1,402
1,402
1,418
0.2
0.0%
16.1
1.1%
Oil/Gas Steam
16
16
16
-0.1
-0.4%
-0.3
-1.8%
Non-Hydro RE
2,180
2,183
2,178
2.4
0.1%
-2.2
-0.1%
Hydro
329
329
329
0.0
0.0%
0.0
0.0%
Energy Storage
154
154
155
0.2
0.1%
0.4
0.2%
Nuclear
660
660
660
0.0
0.0%
0.0
0.0%
Other
29
29
29
0.0
0.0%
0.0
0.0%
Total
4,891
4,889
4,889
-1.4
0.0%
-1.3
0.0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
3.5.6 Coal and Natural Gas Use for the Electric Power Sector
In this section we discuss the estimated changes in coal use and natural gas use in 2028,
2030, and 2035. Table 3-12 and Table 3-13 present percentage changes in national coal usage by
EGUs by coal supply region and coal rank, respectively. These fuel use estimates reflect virtually
no reduction in coal use in the proposed rule relative to the baseline in 2028, and very modest
484
484
454
-0.3
-0.1%
-29.9
-6.2%
1,773
1,774
1,802
0.7
0.0%
28.5
1.6%
30
30
28
0.0
0.1%
-1.6
-5.5%
964
964
967
-0.3
0.0%
3.1
0.3%
294
294
292
-0.2
-0.1%
-1.5
-0.5%
68
69
76
0.3
0.5%
7.7
11.3%
765
765
765
0.0
0.0%
0.0
0.0%
30
30
30
0.0
0.0%
0.0
0.0%
4,409
4,409
4,415
0.2
0.0%
6.3
0.1%
309
307
292
-1.6
-0.5%
-17.1
-5.5%
1,771
1,774
1,783
2.3
0.1%
12.1
0.7%
33
33
33
-0.1
-0.5%
0.4
1.1%
1,269
1,268
1,274
-0.4
0.0%
5.1
0.4%
303
303
303
-0.1
0.0%
0.0
0.0%
98
98
99
0.1
0.1%
1.2
1.2%
734
734
734
0.0
0.0%
0.0
0.0%
29
29
29
0.0
0.0%
0.0
0.0%
4,545
4,545
4,546
0.3
0.0%
1.6
0.0%
3-19
-------
reductions in coal use in 2030 and 2035. All regulatory options reflect a continuing trend of
decreasing coal use nationwide; between 2015 and 2021, annual coal consumption in the electric
power sector fell between 8 and 19 percent annually.28 The proposed rule is projected to result in
up to a 3 percent decrease in coal use in 2035 relative to the baseline. Additionally, the proposed
rule is not projected to result in significant coal switching between supply regions or coal rank.
Table 3-12 2028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Coal Supply
Region for the Baseline Run and the Proposed Rule and More Stringent Alternative
Million Tons
Percent Change from
Baseline
Year
Baseline Run
Proposed
Rule
More-
Stringent Alt.
Proposed
Rule
More-
Stringent Alt.
Appalachia
48.4
48.3
45.3
-0.2%
-6.3%
Interior
50.6
50.5
47.8
0.0%
-5.5%
Waste Coal
2028
4.3
4.3
4.3
0.0%
0.0%
West
148.0
148.0
137.6
0.0%
-7.0%
Total
251.3
251.2
235.1
0.0%
-6.4%
Appalachia
28.2
27.6
26.7
-2.1%
-5.3%
Interior
36.6
36.6
34.6
0.0%
-5.4%
Waste Coal
2030
4.3
4.3
4.3
0.0%
0.0%
West
106.8
106.7
99.3
-0.1%
-7.0%
Total
176.0
175.3
165.0
-0.4%
-6.2%
Appalachia
10.9
10.9
10.0
0.0%
-7.9%
Interior
19.6
19.7
18.2
0.7%
-7.3%
Waste Coal
2035
2.0
1.9
2.0
-3.4%
-0.2%
West
47.9
45.3
39.4
-5.3%
-17.8%
Total
80.4
77.9
69.6
-3.1%
-13.4%
28 U.S. EIA Monthly Energy Review, Table 6.2, January 2022.
3-20
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Table 3-13 2028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Rank for the
Baseline Run and the Proposed Rule and More Stringent Alternative
Million Tons
Percent Change from
Baseline
Rank
Year
Baseline Run
Proposed
Rule
More-
Stringent Alt.
Proposed
Rule
More-
Stringent
Alt.
Bituminous
94.0
93.9
88.2
-0.1%
-6.2%
Subbituminous
Lignite
2028
126.3
27.2
126.3
27.2
117.5
25.6
0.0%
-0.2%
-6.9%
-6.1%
Total
247.5
247.4
231.3
0.0%
-6.5%
Bituminous
59.5
58.9
56.1
-1.0%
-5.8%
Subbituminous
Lignite
2030
86.8
25.4
86.7
25.4
80.7
23.9
-0.1%
0.0%
-7.0%
-5.9%
Total
171.6
171.0
160.6
-0.4%
-6.4%
Bituminous
25.2
25.3
22.9
0.5%
-8.9%
Subbituminous
Lignite
2035
35.9
17.4
33.3
17.4
27.4
17.3
-7.1%
0.0%
-23.7%
-0.2%
Total
78.4
76.0
67.6
-3.1%
-13.8%
Table 3-14 presents the projected changes in national natural gas usage by EGUs in the
2028, 2030, and 2035 run years. These fuel use estimates reflect very modest changes to
projected gas generation in 2028, 2030 and 2035.
Table 3-14 2028, 2030, and 2035 Projected U.S. Power Sector Natural Gas Use for the
Baseline Run and the Proposed Rule and More Stringent Alternative
Trillion Cubic Feet
Percent Change from Baseline
More-Stringent
More-Stringent
Year
Baseline Run
Proposed Rule
Alternative
Proposed Rule
Alternative
2028
12.5
12.5
12.7
0.0%
1.3%
2030
12.6
12.7
12.7
0.1%
0.5%
2035
9.9
9.9
10.0
-0.1%
0.9%
3.5.7 Fuel Price, Market, and Infrastructure
The projected impacts of the proposed rule and more stringent alternative on coal and
natural gas prices are presented below in Table 3-15 and Table 3-16, respectively. As with the
projected impact on fuel use, the projected impact of the proposed rule on minemouth and
delivered coal prices is very small. The small increase in the national weighted average price of
3-21
-------
coal reflects the small projected decrease in the use of western subbituminous coal (see Table
3-12) which is characterized by a lower price on a MMBtu basis than bituminous coal.
Table 3-15 2028, 2030, and 2035 Projected Minemouth and Power Sector Delivered Coal
Price (2019 dollars) for the Baseline and the Proposed Rule and More Stringent Alternative
$/MMBtu
Percent Change from
Baseline
Year
Baseline
Proposed
Rule
More-
Stringent
Alternative
Proposed
Rule
More-
Stringent
Alternative
Minemouth
Delivered
2028
1.16
1.59
1.16
1.59
1.15
1.57
0.00%
0.00%
-0.50%
-1.50%
Minemouth
Delivered
2030
1.17
1.47
1.17
1.47
1.18
1.47
-0.10%
-0.20%
0.60%
0.00%
Minemouth
Delivered
2035
1.34
1.38
1.35
1.40
1.38
1.40
0.90%
1.70%
2.90%
2.00%
Consistent with the projected change in natural gas use under the proposed rule, Henry
Hub and power sector delivered natural gas prices are not projected to significantly change under
the proposed rule over the period analyzed. Under the more stringent alternative, the small
projected increase in natural gas demand is projected to result in a similarly small impact on
average natural gas prices. Table 3-16 summarizes the projected impacts on Henry Hub and
delivered natural gas prices in 2028, 2030, and 2035.
Table 3-16 2028, 2030, and 2035 Projected Henry Hub and Power Sector Delivered
Natural Gas Price (2019 dollars) for the Baseline and the Proposed Rule and More
Stringent Alternative
$/MMBtu
Percent Change from
Baseline
Year
Baseline
Proposed
Rule
More-
Stringent
Alternative
Proposed
Rule
More-
Stringent
Alternative
Henry Hub
Delivered
2028
2.98
3.02
2.98
3.02
3.03
3.08
0.00%
0.00%
1.80%
2.00%
Henry Hub
Delivered
2030
2.41
2.53
2.41
2.53
2.45
2.57
0.00%
0.00%
1.70%
1.50%
Henry Hub
Delivered
2035
1.88
2.10
1.89
2.10
1.89
2.10
0.10%
0.09%
0.20%
0.10%
3-22
-------
3.5.8 Retail Electricity Prices
EPA estimated the change in the retail price of electricity (2019 dollars) using the Retail
Price Model (RPM).29 The RPM was developed by ICF for EPA and uses the IPM estimates of
changes in the cost of generating electricity to estimate the changes in average retail electricity
prices. The prices are average prices over consumer classes (i.e., consumer, commercial, and
industrial) and regions, weighted by the amount of electricity used by each class and in each
region. The RPM combines the IPM annual cost estimates in each of the 64 IPM regions with
EIA electricity market data for each of the 25 electricity supply regions (shown in Figure 3-1) in
the electricity market module of the National Energy Modeling System (NEMS).30
Table 3-17, Table 3-18, and Table 3-19 present the projected percentage changes in the
retail price of electricity for the regulatory control alternatives in 2028, 2030, and 2035,
respectively. Consistent with other projected impacts presented above, average retail electricity
prices at both the national and regional level are projected to be small in each year. In 2028, EPA
estimates that this proposed rule will result in a one tenth of one percent increase in national
average retail electricity price, or by less than one tenth of one mill/kWh.
29 See documentation available at: https://www.epa.gov/airmarkets/retail-price-model
30 See documentation available at:
https://www.eia.gov/outlooks/aeo/nems/documentation/electricity/pdf/m068(2020).pdf
3-23
-------
Table 3-17 Average Retail Electricity Price by Region for the Baseline and the Proposed
Rule and More Stringent Alternative, 2028
All Sectors
2028 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline
Proposed Rule
More-Stringent
Alternative
Proposed Rule
More-Stringent
Alternative
TRE
99.4
99.3
100.0
-0.1%
0.6%
FRCC
99.7
99.7
100.3
0.0%
0.6%
MISW
79.4
79.4
79.9
0.0%
0.6%
MISC
101.7
101.7
103.2
0.0%
1.5%
MISE
123.0
123.1
123.7
0.0%
0.6%
MISS
105.1
105.0
105.7
0.0%
0.6%
ISNE
142.3
142.1
144.0
-0.1%
1.2%
NYCW
213.4
211.8
212.1
-0.7%
-0.6%
NYUP
142.1
141.1
141.7
-0.6%
-0.2%
PJME
121.5
121.7
123.9
0.1%
2.0%
PJMW
105.5
106.3
109.7
0.7%
3.9%
PJMC
92.3
92.4
93.4
0.0%
1.1%
PJMD
82.8
83.6
86.9
1.0%
5.0%
SRCA
109.8
109.9
110.1
0.0%
0.3%
SRSE
112.1
112.1
112.2
0.0%
0.1%
SRCE
74.2
74.2
74.1
0.0%
-0.1%
SPPS
85.4
85.5
85.3
0.1%
-0.1%
SPPC
84.1
84.0
83.2
0.0%
-1.0%
SPPN
77.3
77.3
77.4
0.0%
0.2%
SRSG
92.8
92.8
93.2
0.0%
0.4%
CANO
149.9
149.9
150.2
0.0%
0.2%
CASO
198.7
198.7
198.9
0.0%
0.1%
NWPP
78.3
78.5
78.7
0.3%
0.6%
RMRG
87.3
87.3
88.4
0.0%
1.3%
BASN
86.5
86.5
86.3
0.1%
-0.2%
National
107.0
107.0
107.9
0.1%
0.9%
3-24
-------
Table 3-18 Average Retail Electricity Price by Region for the Baseline and the Proposed
Rule and More Stringent Alternative, 2030
All Sectors
2030 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline
Proposed Rule
More-Stringent
Alternative
Proposed Rule
More-Stringent
Alternative
TRE
78.4
78.4
78.6
0.0%
0.2%
FRCC
88.7
88.7
89.2
0.0%
0.5%
MISW
80.5
80.5
80.5
0.0%
0.0%
MISC
88.9
88.9
88.9
0.0%
0.0%
MISE
96.7
96.8
99.1
0.1%
2.5%
MISS
89.4
89.4
89.6
0.0%
0.3%
ISNE
146.9
146.9
147.1
0.0%
0.2%
NYCW
202.3
202.9
202.9
0.3%
0.3%
NYUP
121.6
121.9
121.9
0.3%
0.3%
PJME
101.5
102.1
102.1
0.5%
0.5%
PJMW
94.0
94.0
94.3
0.0%
0.3%
PJMC
77.8
77.8
80.6
0.1%
3.6%
PJMD
72.3
72.3
71.9
0.0%
-0.6%
SRCA
96.8
96.8
96.7
0.0%
0.0%
SRSE
90.4
90.4
90.5
0.0%
0.1%
SRCE
104.9
104.9
105.1
0.0%
0.2%
SPPS
69.0
69.0
68.9
0.0%
-0.1%
SPPC
80.3
80.3
80.4
0.0%
0.2%
SPPN
59.9
59.9
59.8
0.0%
-0.2%
SRSG
83.0
83.0
83.0
0.1%
0.1%
CANO
154.8
154.8
154.7
0.0%
-0.1%
CASO
187.0
186.9
187.4
0.0%
0.2%
NWPP
73.8
73.9
74.1
0.2%
0.4%
RMRG
86.4
86.5
87.1
0.1%
0.9%
BASN
88.4
88.5
89.3
0.1%
1.0%
National
97.0
97.0
97.3
0.1%
0.3%
3-25
-------
Table 3-19 Average Retail Electricity Price by Region for the Baseline and the Proposed
Rule and More Stringent Alternative, 2035
All Sectors
2035 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline
Proposed Rule
More-Stringent
Alternative
Proposed Rule
More-Stringent
Alternative
TRE
68.3
68.3
68.3
0.0%
0.0%
FRCC
81.0
81.0
81.0
0.0%
0.1%
MISW
80.2
80.2
80.3
0.0%
0.0%
MISC
80.2
80.2
80.2
0.0%
0.1%
MISE
88.9
88.8
88.9
0.0%
0.0%
MISS
84.4
84.4
84.5
0.0%
0.0%
ISNE
150.4
150.4
150.4
0.0%
0.0%
NYCW
187.2
187.2
187.3
0.0%
0.0%
NYUP
106.7
106.7
106.7
0.0%
0.0%
PJME
105.3
105.2
105.2
0.0%
0.0%
PJMW
82.4
82.3
82.3
0.0%
-0.1%
PJMC
82.4
82.4
82.5
0.0%
0.1%
PJMD
73.3
73.3
73.2
0.0%
-0.1%
SRCA
92.9
92.9
93.0
0.0%
0.1%
SRSE
113.5
113.5
113.5
0.0%
0.0%
SRCE
69.1
69.1
69.1
0.0%
0.0%
SPPS
70.3
70.3
70.4
0.0%
0.1%
SPPC
67.9
67.9
67.9
0.0%
0.1%
SPPN
62.8
62.8
62.9
0.0%
0.0%
SRSG
93.5
93.5
93.5
0.0%
0.0%
CANO
150.9
150.9
150.9
0.0%
0.0%
CASO
177.8
177.8
177.8
0.0%
0.0%
NWPP
79.6
79.6
79.6
0.0%
0.0%
RMRG
91.5
91.5
91.6
0.0%
0.1%
BASN
78.2
78.5
79.1
0.3%
1.1%
National
92.7
92.8
92.8
0.0%
0.0%
3-26
-------
NWPP
MISW,
<9
SPPN
MISE;
NYUP
12
gjMC1
¦21 ¦
[CANO]
mom
pjme]
NYCW
25
BASH
24
RMRGj
13
PJMD
Figure 3-1 Electricity Market Module Regions
Source: EIA (http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf)
3.6 Limitations of Analysis and Key Areas of Uncertainty
EPA's power sector modeling is based on expert judgment of various input assumptions
for variables whose outcomes are uncertain. As a general matter, the Agency reviews the best
available information from engineering studies of air pollution controls and new capacity
construction costs to support a reasonable modeling framework for analyzing the cost, emission
changes, and other impacts of regulatory actions for EGUs. The annualized cost of the proposed
rule, as quantified here, is EPA's best assessment of the cost of implementing the proposed rule
on the power sector.
The IPM-projected annualized cost estimates of private compliance costs provided in this
analysis are meant to show the increase in production (generating) costs to the power sector in
response to the proposed rule. To estimate these annualized costs, as discussed earlier, the EPA
uses a conventional and widely accepted approach that applies a CRF multiplier to capital
investments and adds that to the annual incremental operating expenses to calculate annual costs
The CRF is derived from estimates of the cost of capital (private discount rate), the amount of
3-27
-------
insurance coverage required, local property taxes, and the life of capital. The private compliance
costs presented earlier are EPA's best estimate of the direct private compliance costs of the rule.
In addition, there are several key areas of uncertainty related to the electric power sector
that are worth noting, including:
• Electricity demand: The analysis includes an assumption for future electricity demand.
To the extent electricity demand is higher and lower, it may increase/decrease the
projected future composition of the fleet.
• Natural gas supply and demand: To the extent natural gas supply and delivered prices
are higher or lower, it would influence the use of natural gas for electricity generation and
overall competitiveness of other EGUs (e.g., coal and nuclear units).
• Longer-term planning by utilities: Many utilities have announced long-term clean
energy and/or climate commitments, with a phasing out of large amounts of coal capacity
by 2030 and continuing through 2050. These announcements, some of which are not
legally binding, are not necessarily reflected in the baseline, and may alter the amount of
coal capacity projected in the baseline that would be covered under this proposed rule or
the more stringent alternative.
• Filterable PM emissions and control: As discussed above, the baseline filterable PM
emissions rates for each unit are based on the analysis documented in the memorandum
titled: "2023 Technology Review for the Coal- and Oil-Fired EGU Source Category." For
those EGUs with rates greater than the proposed limit or more stringent alternative, EPA
assumes that control technology summarized in Section 3.4 would be necessary to remain
operational. While the baseline emissions rate for each EGU and the cost and
performance assumption for each PM control technology are the best available to EPA at
this time, it is possible that some EGUs may be able to achieve the proposed or
alternative filterable PM emissions limits with less costly control technology (e.g., an
ESP upgrade instead of a fabric filter installation). It is also possible that EPA's cost
assumptions reflect higher technology costs than might be incurred by EGUs.
These are key uncertainties that may affect the overall composition of electric power
generation fleet and/or compliance with the proposed emissions limits and could thus have an
effect on the estimated costs and impacts of this proposed action. While it is important to
recognize these key areas of uncertainty, they do not change the EPA's overall confidence in the
projected impacts of the proposed rule presented in this section. EPA continues to monitor
industry developments and makes appropriate updates to the modeling platforms in order to
reflect the best and most current data available.
3-28
-------
The impacts of the Later Model Year Light-Duty Vehicle GHG Emissions Standards31 are
not captured in the baseline. This rule is projected to increase the total demand for electricity by
0.5 percent in 2030 and 1 percent in 2040 relative to 2020 levels.32 This translates into a 0.4
percent increase in electricity demand in 2030 and a 0.8 percent increase in electricity demand in
2040 relative to the baseline electricity demand projections assumed in this analysis. The impacts
of the Proposed Standards of Performance for New, Reconstructed, and Modified Sources and
Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review33 are
also not included in this analysis. Inclusion of these standards would likely increase the price of
natural gas modestly as a result of limitations on the usage of reciprocating internal combustion
engines in the pipeline transportation of natural gas. All else equal inclusion of these two
programs would likely result in a modest increase in the total cost of compliance for this rule.
3.7 References
U.S. EPA. (2005). Regulatory Impact Analysis for the Final Clean Air Interstate Rule. Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Health and Environmental Impact Division. Available at:
https://www.epa.gov/sites/default/files/2020-07/documents/transport riafinal-clean-air-
interstate-rule2005-03.pdf
U.S. EPA. (201 la). Regulatory Impact Analysis for the Federal Implementation Plans to Reduce
Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of
SIP Approvals for 22 States. Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division. Available at: https://www3.epa.gov/ttn/ecas/docs/ria/transport ria final-
csapr 201 l-06.pdf
U.S. EPA. (201 lb). Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards.
(EPA-452/R-11-011). Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf
U.S. EPA. (2014). Economic Analysis for the Final Section 316(b) Existing Facilities Rule.
(EPA-821-R-14-001). Washington DC: U.S. Environmental Protection Agency.
Available at: https://www.epa.gov/sites/default/files/2015-05/documents/cooling-
waterjphase-4economics 2014.pdf
31 Available at: https://www.federalregister.gov/documents/2021/08/10/2021-16582/revised-2023-and-later-model-
year-light-duty-vehicle-greenhouse-gas-emissions-standards
32 Regulatory Impact Analysis available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1012ONB.pdf
33 Available at: https://www.federalregister.gov/documents/2021/11/15/2021 -24202/standards-of-performance-for-
new-reconstructed-and-modified-sources-and-emissions-guidelines-for
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U.S. EPA. (2015a). Benefit and Cost Analysis for the Effluent Limitations Guidelines and
Standards for the Steam Electric Power Generating Point Source Category. (EPA-821-
R-15-005). Washington DC: U.S. Environmental Protection Agency. Available at:
https://www.epa.gov/sites/default/files/2015-10/documents/steam-electric benefit-cost-
analysis09-29-2015.pdf
U.S. EPA. (2015b). Regulatory Impact Analysis for the Clean Power Plan Final Rule. (EPA-
452/R-l5-003). Research Triangle Park, NC: U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Health and Environmental Impact
Division. Available at: https://www.epa.gov/sites/default/files/2020-
07 documents utilities ria final-clean-power-plan-exist ing-units 2015-08.pdf
U.S. EPA. (2015c). Regulatory Impact Analysis: EPA's 2015 RCRA Final Rule Regulating Coal
Combustion Residual (CCR) Landfills and Surface Impoundments At Coal-Fired Electric
Utility Power Plants. (EPA-821-R-20-003). Washington DC: U.S. Environmental
Protection Agency. Available at: https://www.regulations.gov/document/EPA-HQ-RCRA-
2009-0640-12034
U.S. EPA. (2016). Regulatory Impact Analysis of the Cross-State Air Pollution Rule (CSAPR)
Update for the 2008 National Ambient Air Quality Standards for Ground-Level Ozone.
(EPA-452/R-16-004). Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division. Available at: https://www.epa.gov/sites/default/files/2020-
07/documents/transport riaJinal-csapr-update 2016-09.pdf
U.S. EPA. (2019). Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the
Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility
Generating Units. (EPA-452/R-19-003). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Health
and Environmental Impact Division. Available at:
https://www.epa.gov/sites/production/files/2019-
06/documents/utilities riaJinal cpp repeal and ace 2019-06.pdf
U.S. EPA. (2020). Benefit and Cost Analysis for Revisions to the Effluent Limitations Guidelines
and Standards for the Steam Electric Power Generating Point Source Category. (EPA-
821-R-20-003). Washington DC: U.S. Environmental Protection Agency. Available at:
https://www. epa. gov/sites/default/files/2020-
08/documents/steam electric elg 2020jinal reconsideration rule benefit and cost an
alysis.pdf
U.S. EPA. (2021). Regulatory Impact Analysis for the Final Revised Cross-State Air Pollution
Rule (CSAPR) Update for the 2008 Ozone NAAQS. (EPA-452/R-21-002). Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Health and Environmental Impact Division. Available at:
https://www. epa. gov/sites/default/files/2021-
03/documents/revised csapr update ria final.pdf
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4 BENEFITS ANALYSIS
4.1 Introduction
This proposed rule is projected to reduce emissions of mercury and non-mercury metal
HAP, fine particulate matter (PM2.5), sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon
dioxide (CO2) nationwide relative to emissions in the Post-IRA 2022 Reference Case that
constitutes the baseline for this RIA. The projected reductions in mercury are expected to reduce
the bioconcentration of methylmercury in fish. Subsistence fishing is associated with vulnerable
populations, including minorities and those of low socioeconomic status. Further reductions in
mercury emissions should reduce fish concentrations and exposure to HAP particularly for the
subsistence fisher sub-population. The projected reductions in HAP emissions should help EPA
maintain an ample margin of safety by reducing exposure to methylmercury and carcinogenic
metal HAP.
Regarding the potential benefits of the rule from projected HAP reductions, we note that
these are discussed only qualitatively and not quantitatively. Exposure to the HAP emitted by the
source category, depending on the exposure duration and level of exposure, is associated with a
variety of adverse health effects. These adverse health effects may include chronic health
disorders (e.g., irritation of the lung, skin, and mucus membranes; decreased pulmonary function,
pneumonia, or lung damage; detrimental effects on the central nervous system; cardiovascular
disease; damage to the kidneys; and alimentary effects such as nausea and vomiting), adverse
neurodevelopmental impacts, and increased risk of cancer. See 76 FR 25003-25005 for a fuller
discussion of the health effects associated with HAP pollutants.
The analysis of the overall EGU sector completed for EPA's review of the 2020
appropriate and necessary finding (2023 Final A&N Review) identified significant reductions in
cardiovascular and neuro-developmental effects from exposure to methylmercury (88 FR
13956). However, the amount of mercury reduction expected is a fraction of the mercury
estimates used in the 2023 Final A&N Review. Overall, the uncertainty associated with
modeling potential benefits of mercury reduction for fish consumers would be sufficiently large
as to compromise the utility of those benefit estimates—though importantly such uncertainty
does not decrease our confidence that reductions in emissions should result in reduced exposures
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of HAP to the general population, including methylmercury exposures to subsistence fishers
located near these facilities. Further, estimated risks from exposure to non-mercury metal HAP
were not expected to exceed acceptable levels, s , although we note that these emissions
reductions should result in decreased exposure to HAP for individuals living near these facilities.
Reducing emissions of fine PM2.5 and SO2 emissions is expected to reduce ground-level
PM2.5 concentrations. Reducing NOx emissions is expected to reduce both ground-level ozone
and PM2.5 concentrations. Below we present the estimated number and economic value of these
avoided PM2.5 and ozone-attributable premature deaths and illnesses. We also present the
estimated monetized climate and health benefits associated with emission reductions for each of
three regulatory options described in prior sections.
In addition to reporting results, this section details the methods used to estimate the
benefits to human health of reducing concentrations of PM2.5 and ozone resulting from the
projected emissions reductions from EGUs under this proposal. This analysis uses methods for
determining air quality changes that has been used in the RIAs from multiple previous proposed
and final rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022c). The approach involves two major
steps: (1) developing spatial fields of air quality across the U.S. for a baseline scenario and the
proposed and more stringent regulatory options examined in this RIA for 2028, 2030 and 2035
using nationwide photochemical modeling and related analyses; and (2) using these spatial fields
in BenMAP-CE to quantify the benefits under each regulatory control alternative and each year
as compared to the baseline in that year.34 See Section 4.3.3 for more detail on BenMAP-CE.
When estimating the value of improved air quality over a multi-year time horizon, the analysis
applies population growth and income growth projections for each future year through 2037 and
estimates of baseline mortality incidence rates at five-year increments.
Elevated concentrations of GHGs in the atmosphere have been warming the planet,
leading to changes in the Earth's climate including changes in the frequency and intensity of heat
waves, precipitation, and extreme weather events, rising seas, and retreating snow and ice. The
well-documented atmospheric changes due to anthropogenic GHG emissions are changing the
climate at a pace and in a way that threatens human health, society, and the natural environment.
34 Note we do not perform air quality analysis on the less stringent regulatory option because it has no quantified
emissions reductions associated with the proposed requirements for CEMS and the removal of startup definition
number two.
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There will be important climate benefits associated with the CO2 emissions reductions expected
from this proposed rule. Climate benefits from reducing emissions of CO2 can be monetized
using estimates of the SC-CO2.
Though the proposed rule is likely to also yield positive benefits associated with reducing
pollutants other than mercury, non-mercury metal HAP, PM2.5, ozone, and CO2, time, resource,
and data limitations prevented us from quantifying and estimating the economic value of those
reductions. Specifically, in this RIA EPA does not monetize health benefits of reducing direct
exposure to NO2 and SO2 nor ecosystem effects and visibility impairment associated with
changes in air quality. In addition, this RIA does not include monetized impacts from changes in
pollutants in other media, such as water effluents. We qualitatively discuss these unquantified
impacts in this section.
4.2 Hazardous Air Pollutant Benefits
This proposed rule is projected to reduce emissions of mercury and non-mercury metal
HAP. Specifically, projected reductions in mercury are expected to help reduce exposure to
methylmercury for sub-populations that rely on subsistence fishing. In addition, projected
emissions reductions should also reduce exposure to non-mercury metal HAP including
carcinogens such as nickel, arsenic, and hexavalent chromium, for residents located in the
vicinity of these facilities.
4.2.1 Mercury
Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in
three forms: gaseous elemental mercury (HgO), oxidized mercury compounds (Hg+2), and
particle-bound mercury (HgP). Elemental mercury does not quickly deposit or chemically react
in the atmosphere, resulting in residence times that are long enough to contribute to global scale
deposition. Oxidized mercury and HgP deposit quickly from the atmosphere impacting local and
regional areas in proximity to sources. Methylmercury is formed by microbial action in the top
layers of sediment and soils, after mercury has precipitated from the air and deposited into
waterbodies or land. Once formed, methylmercury is taken up by aquatic organisms and
bioaccumulates up the aquatic food web. Larger predatory fish may have methylmercury
concentrations many times, typically on the order of one million times, that of the concentrations
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in the freshwater body in which they live. Methylmercury can adversely impact ecosystems and
wildlife.
Human exposure to methylmercury is known to have several adverse
neurodevelopmental impacts, such as IQ loss measured by performance on neurobehavioral tests,
particularly on tests of attention, fine motor-function, language, and visual spatial ability. In
addition, evidence in humans and animals suggests that methylmercury can have adverse effects
on both the developing and the adult cardiovascular system, including fatal and non-fatal
ischemic heart disease (IHD). Further, nephrotoxicity, immunotoxicity, reproductive effects
(impaired fertility), and developmental effects have been observed with methylmercury exposure
in animal studies (Agency for Toxic Substances and Disease Registry, 2022). Methylmercury has
some genotoxic activity and is capable of causing chromosomal damage in a number of
experimental systems. The EPA has classified methylmercury as a "possible" human carcinogen.
The projected reductions in mercury under this proposed rule are expected to reduce the
bioconcentration of methylmercury in fish due to mercury emissions from MATS-affected
sources. Risk from near-field deposition of mercury to subsistence fishers has previously been
evaluated, using a site-specific assessment of a lake near three lignite-fired facilities (U.S. EPA,
2020d). The results suggest that methylmercury exposure to subsistence fishers from lignite-fired
units is below the current reference dose (RfD) for methylmercury neurodevelopmental toxicity
or IQ loss, with an estimated hazard quotient (HQ) of 0.06. In general, the EPA believes that
exposures at or below the RfD are unlikely to be associated with appreciable risk of deleterious
effects. However, no RfD defines an exposure level corresponding to zero risk; moreover, the
RfD does not represent a bright line above which individuals are at risk of adverse effects. In
addition, there was no evidence of a threshold for methylmercury-related neurotoxicity within
the range of exposures in the Faroe Islands study which served as the primary basis for the RfD
(U.S. EPA, 2001).
Regarding the potential magnitude of human health risk reductions and benefits
associated with this proposed rule, we make the following observations. All of the exposure
results generated as part of the 2020 Residual Risk analysis were below the presumptive
acceptable cancer risk threshold and noncancer health-based thresholds. While these results
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suggest that the residual risks from HAP exposure are low, we do recognize that this proposed
regulation should still reduce exposure to HAP.
Regarding potential benefits of the rule to the general population of fish consumers, while
we note that the analysis of the overall EGU sector completed for the 2023 Final A&N Review
did identify significant reductions in cardiovascular and neuro-developmental effects, given the
substantially smaller mercury reduction associated with this proposed rule (approximately 60 to
80 pounds per year under the proposal compared to the approximately 29 tons of mercury
evaluated in the 2023 Final A&N Review), overall uncertainty associated with modeling
potential benefits for the broader population of fish consumers would be sufficiently large as to
compromise the utility of those benefit estimates.
Despite the lack of quantifiable risks from mercury emissions, reductions would be
expected to have some impact (reduction) on the overall methylmercury burden in fish for
waterbodies near covered facilities. In the Appropriate and Necessary determination, EPA
illustrated that the burden of mercury exposure is not equally distributed across the population
and that some subpopulations bore disproportionate risks associated with exposure to emissions
from U.S. EGUs. High levels of fish consumption observed with subsistence fishing were
associated with vulnerable populations, including minorities and those with low socioeconomic
status (SES). Reductions in mercury emissions should reduce methylmercury exposure and body
burden for subsistence fishers.
U.S. EGU mercury emissions can lead to increased deposition of mercury to nearby
waterbodies. Deposition of mercury to waterbodies can also have an impact on ecosystems and
wildlife. Mercury contamination is present in all environmental media with aquatic systems
being particularly impacted due to bioaccumulation. Bioaccumulation refers to the net uptake of
a contaminant from all possible pathways and includes the accumulation that may occur by direct
exposure to contaminated media as well as uptake from food. Atmospheric mercury enters
freshwater ecosystems by direct deposition and through runoff from terrestrial watersheds. Once
mercury deposits, it may be converted to organic methylmercury mediated primarily by sulfate-
reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly
increasing mercury toxicity and potential to bioaccumulate in aquatic foodwebs (Munthe et al.
2007). The highest levels of methylmercury accumulation are most often measured in fish eating
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(piscivorous) animals and those which prey on other fish eaters. In laboratory studies, adverse
effects from exposure to methylmercury in wildlife have been observed in fish, mink, otters, and
several avian species at exposure levels as low as 0.25 jag/g bw/day (U.S. EPA, 1997). The risk
of mercury exposure may also extend to insectivorous terrestrial species such as songbirds, bats,
spiders, and amphibians that receive mercury deposition or from aquatic systems near the forest
areas they inhabit (Bergeron et al., 2010, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al.,
2009 & 2010). The proposed emissions reductions of mercury are expected to lower deposition
of mercury into ecosystems and reduce U.S. EGU attributable bioaccumulation of
methylmercury in wildlife, particularly for areas closer to the effected units subject to near-field
deposition. Because mercury emissions from U.S. EGUs can both become deposited in or
bioaccumulate in organisms living in foreign and international waters, reduction of mercury
emissions from U.S. EGUs could lead to some benefits internationally as well. EPA is currently
unable to quantify or monetize such effects.
4.2.2 Metal HAP
U.S. EGUs are the largest source of selenium (Se) emissions and a major source of
metallic HAP emissions including arsenic (As), chromium (Cr), nickel (Ni), and cobalt (Co).
Additionally, U.S. EGUs emit cadmium (Cd), beryllium (Be), lead (Pb), and manganese (Mn).
These emissions include metal HAP that are persistent and bioaccumulative (Cd, As, and Pb) and
others have the potential to cause cancer (Ni, Cr, Cd, Be, Co, and Pb). PM controls are expected
to reduce metal HAP emissions and therefore reduce exposure to metal HAP for the general
population including those living near these facilities.
Exposure to these metal HAP, depending on exposure duration and levels of exposures, is
associated with a variety of adverse health effects. These adverse health effects may include
chronic health disorders (e.g., irritation of the lung, skin, and mucus membranes; decreased
pulmonary function, pneumonia, or lung damage; detrimental effects on the central nervous
system; damage to the kidneys; and alimentary effects such as nausea and vomiting). As of 2023,
three of the key metal HAP or their compounds emitted by EGUs (As, Cr, and Ni) have been
classified as human carcinogens, while two others (Cd and Se) are classified as probable human
carcinogens. See 76 FR 25003-25005 for a fuller discussion of the health effects associated with
these pollutants.
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The emission estimates for this source category were obtained in 2020 from two main
sources: EPA's Air Markets Program Data and EPA's WebFIRE. U.S. EGU source category
emissions of non-mercury HAP are not expected to exceed 1 in a million for inhalation cancer
risk for those facilities impacted by the proposed controls.. Further, cancer risk was determined
to fall within the acceptable range for multipathway exposure to the persistent and
bioaccumulative non-mercury metal HAP, such as arsenic, cadmium, and lead.35 However, the
proposed controls should reduce levels of exposure to carcinogenic HAP in communities near
the impacted facilities.
EPA also evaluated the potential for noncancer risks from exposure to non-mercury metal
HAP in 2020. To address the risk from chronic inhalation exposure to multiple pollutants, we
aggregated the health risks associated with pollutants that affect the same target organ. Further,
we examined the potential for adverse health effects from acute inhalation exposure to individual
pollutants. Lastly, we also examined the potential for health impacts stemming from multiple
pathways of exposure for arsenic, cadmium, and lead. The estimated risks were not expected to
exceed current health thresholds for adverse effects (U.S. EPA, 2020d). Therefore, we are unable
to identify or quantify noncancer benefits from the proposed non-mercury metal HAP emission
reductions, although we do note that emissions reductions associated with this rule should further
reduce exposure to these non-mercury metal HAP in communities near these facilities.
4.2.3 Additional HAP Benefits
As discussed in detail in the 2023 Final A&N Review, it is challenging to quantify the
full range of benefits of HAP reductions. But that does not mean that these benefits are small,
insignificant, or nonexistent. In the 2011 MATS RIA (U.S. EPA, 2011), EPA discussed the
potential for non-monetizable benefits from effects on fish, birds, and mammals, in part
represented through the commercial and recreational fishing economy. A report submitted to
EPA in comment concluded that recreational and commercial fishing are substantial contributors
to regional U.S. economies with dollar values in the tens of billions (IEc, 2019). At this scale of
economic activity, even small shifts in consumer behavior prompted by further HAP reductions
can result in substantial economic impacts.
35 https://www.regulations.gOv/document/EPA-HQ-OAR-2018-0794-0014
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As another example of the potential value of these emissions reductions, EPA received
numerous comments in the public comment periods of past EGU HAP regulation highlighting
that benefits of mercury reductions to tribal health, subsistence, fishing rights, and cultural
identity, while not easily quantified or monetized, are nonetheless important to consider. Finally,
EPA also qualitatively considers impacts on ecosystem services, which are generally defined as
the economic benefits that individuals and organizations obtain from ecosystems. The
monetization of endpoints like ecosystem services, tribal culture, and the activity related to
fishing remains challenging. While EPA is not able to monetize the impacts of reduced HAP
exposures resulting from this proposal, we note the importance of the contributions of further
reductions of HAP emissions to the sustainability of these important economic and cultural
values.
4.3 Criteria Pollutant Benefits
The benefits analysis presented in this section applies methods consistent with those
applied most recently in the RIA for the proposed PM National Ambient Air Quality Standards
(NAAQS). EPA's approach for selecting PM2.5 and ozone-related health endpoints to quantify
and monetize is detailed in the interest of brevity, we summarize our approach below and refer
readers to the referenced Health Benefits TSD (U.S. EPA, 2023). In the interest of brevity, we
summarize our approach below and refer readers to the referenced the Health Benefits TSD for a
full description of the methodology.
Estimating the health benefits of reductions in PM2.5 and ozone exposure begins with
estimating the change in exposure for each individual and then estimating the change in each
individual's risks for those health outcomes affected by exposure. The benefit of the reduction in
each health risk is based on the exposed individual's willingness to pay (WTP) for the risk
change, assuming that each outcome is independent of one another. The greater the magnitude of
the risk reduction from a given change in concentration, the greater the individual's WTP, all
else equal. The social benefit of the change in health risks equals the sum of the individual WTP
estimates across all of the affected individuals residing in the U.S.36 We conduct this analysis by
36 This RIA also reports the change in the sum of the risk, or the change in the total incidence, of a health outcome
across the population. If the benefit per unit of risk is invariant across individuals, the total expected change in the
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adapting primary research—specifically, air pollution epidemiology studies and economic value
studies—from similar contexts. This approach is sometimes referred to as "benefits transfer."
Below we describe the procedure we follow for: (1) developing spatial fields of air quality for
baseline and three regulatory control alternatives (2) selecting air pollution health endpoints to
quantify; (3) calculating counts of air pollution effects using a health impact function; (4)
specifying the health impact function with concentration-response parameters drawn from the
epidemiological literature to calculate the economic value of the health impacts. We estimate the
quantity and economic value of air pollution-related effects using a "damage-function." This
approach quantifies counts of air pollution-attributable cases of adverse health outcomes and
assigns dollar values to those counts, while assuming that each outcome is independent of one
another.
As structured, the proposed rule would affect the distribution of ozone and PM2.5
concentrations in much of the U.S. This RIA estimates avoided ozone- and PM2.5-related health
impacts that are distinct from those reported in the RIAs for both ozone and PM NAAQS (U.S.
EPA, 2015c, 2022d) The ozone and PM NAAQS RIAs illustrate, but do not predict, the benefits
and costs of strategies that states may choose to enact when implementing a revised NAAQS;
these costs and benefits are illustrative and cannot be added to the costs and benefits of policies
that prescribe specific emission control measures. This RIA estimates the benefits (and costs) of
specific emissions control measures. The benefit estimates are based on these modeled changes
in PM2.5 and summer season average ozone concentrations.
4.3.1 Air Quality Modeling Methodology
The proposed rule influences the level of pollutants emitted in the atmosphere that
adversely affect human health, including directly emitted PM2.5, as well as SO2 and NOx, which
are both precursors to ambient PM2.5. NOx emissions are also a precursor to ambient ground-
level ozone. EPA used air quality modeling to estimate changes in ozone and PM2.5
concentrations that may occur as a result of the proposed regulatory option and the more
stringent regulatory option in the proposed rule relative to the baseline
incidence of the health outcome across the population can be multiplied by the benefit per unit of risk to estimate the
social benefit of the total expected change in the incidence of the health outcome.
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As described in the Air Quality Modeling Appendix (Appendix A), gridded spatial fields
of ozone and PM2.5 concentrations representing the baseline and two regulatory options were
derived from CAMx source apportionment modeling in combination with NOx, SO2, and
primary PM2.5 EGU emissions obtained from the outputs of the IPM runs described in Section 3
of this RIA. While the air quality modeling includes all inventoried pollution sources in the
contiguous U.S., contributions from all sources other than EGUs are held constant at projected
2026 levels in this analysis, and the only changes quantified between the baseline and the
regulatory options are those associated with the projected impacts of the proposed rule on EGU
emissions. EPA prepared gridded spatial fields of air quality for the baseline and the regulatory
options for two health-impact metrics: annual mean PM2.5 and April through September seasonal
average eight-hour daily maximum (MDA8) ozone (AS-M03). These ozone and PM2.5 gridded
spatial fields cover all locations in the contiguous U.S. and were used as inputs to BenMAP-CE
which, in turn, was used to quantify the benefits from this proposed rule.
The basic methodology for determining air quality changes is the same as that used in the
RIAs from multiple previous rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022c). The Air
Quality Modeling Appendix (Appendix A) provides additional details on the air quality
modeling and the methodologies EPA used to develop gridded spatial fields of summertime
ozone and annual PM2.5 concentrations. The appendix also provides figures showing the
geographical distribution of air quality changes.
4.3.2 Selecting Air Pollution Health Endpoints to Quantify
The methods used in this RIA incorporate evidence reported in the most recent completed
PM and Ozone ISAs and accounts for recommendations from the Science Advisory Board (U.S.
EPA, 2022f). When updating each health endpoint EPA considered: (1) the extent to which there
exists a causal relationship between that pollutant and the adverse effect; (2) whether suitable
epidemiologic studies exist to support quantifying health impacts; (3) and whether robust
economic approaches are available for estimating the value of the impact of reducing human
exposure to the pollutant. Our approach for updating the endpoints and to identify suitable
epidemiologic studies, baseline incidence rates, population demographics, and valuation
estimates is summarized below. Detailed descriptions of these updates are available in the Health
Benefits TSD, which is in the docket for this rulemaking. The Health Benefits TSD fully
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describes the Agency's approach for quantifying the number and value of estimated air
pollution-related impacts. In this document the reader can find the rationale for selecting health
endpoints to quantify; the demographic, health and economic data used; modeling assumptions;
and our techniques for quantifying uncertainty.37
37 The analysis was completed using BenMAP-CE version 1.5.8, which is a variant of the current publicly available
version.
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Table 4-1 Health Effects of Ambient Ozone and PM2.5 and Climate Effects
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Premature mortality
from exposure to
PM2.5
Adult premature mortality based on cohort study
estimates and expert elicitation estimates (age 65-99
or age 30-99)
~
~
PMISA
Infant mortality (age <1)
~
~
PMISA
Heart attacks (age >18)
~
PMISA
Hospital admissions—cardiovascular (ages 65-99)
~
~
PMISA
Emergency department visits— cardiovascular (age
0-99)
~
~
PMISA
Hospital admissions—respiratory (ages 0-18 and 65-
99)
Emergency room visits—respiratory (all ages)
Cardiac arrest (ages 0-99; excludes initial hospital
and/or emergency department visits)
Stroke (ages 65-99)
Asthma onset (ages 0-17)
~
~
~
~
~
~
~
~
PMISA
PMISA
PMISA
PMISA
PMISA
Asthma symptoms/exacerbation (6-17)
~
~
PMISA
Nonfatal morbidity
from exposure to
Lung cancer (ages 30-99)
Allergic rhinitis (hay fever) symptoms (ages 3-17)
Lost work days (age 18-65)
~
~
~
~
~
~
PMISA
PMISA
PMISA
PM2.5
Minor restricted-activity days (age 18-65)
~
~
PMISA
Hospital admissions—Alzheimer's disease (ages 65-
99)
~
~
PMISA
Hospital admissions—Parkinson's disease (ages 65-
99)
~
~
PMISA
Other cardiovascular effects (e.g., other ages)
—
—
PMISA2
Other respiratory effects (e.g., pulmonary function,
non-asthma ER visits, non-bronchitis chronic
diseases, other ages and populations)
—
—
PMISA2
Other nervous system effects (e.g., autism, cognitive
decline, dementia)
—
—
PMISA2
Metabolic effects (e.g., diabetes)
—
—
PMISA2
Reproductive and developmental effects (e.g., low
birth weight, pre-term births, etc.)
—
—
PMISA2
Cancer, mutagenicity, and genotoxicity effects
—
—
PMISA2
Mortality from
Premature respiratory mortality based on short-term
study estimates (0-99)
~
~
Ozone ISA
exposure to ozone
Premature respiratory mortality based on long-term
study estimates (age 30-99)
~
~
Ozone ISA
Hospital admissions—respiratory (ages 0-99)
~
~
Ozone ISA
Emergency department visits—respiratory (ages 0-
99)
Asthma onset (0-17)
Asthma symptoms/exacerbation (asthmatics age 2-
17]
Allergic rhinitis (hay fever) symptoms (ages 3-17)
Minor restricted-activity days (age 18-65)
~
~
Ozone ISA
Nonfatal morbidity
from exposure to
ozone
v'
•/
¦/
•/
v'
•/
¦/
•/
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
School absence days (age 5-17)
•/
•/
Ozone ISA
Decreased outdoor worker productivity (age 18-65)
—
—
Ozone ISA2
Metabolic effects (e.g., diabetes)
—
—
Ozone ISA2
Other respiratory effects (e.g., premature aging of
..lungs)
—
—
Ozone ISA2
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Table 4-1
Health Effects of Ambient Ozone and PM2.5 and Climate Effects
Category
Effect
Effect Effect More
Quantified Monetized Information
Reproductive and developmental effects
Climate impacts from carbon dioxide (CO2)
Cardiovascular and nervous svstem effects
— Ozone ISA2
— Ozone ISA2
S Section 5.2
Climate
effects
Other climate impacts (e.g., ozone, black carbon,
aerosols, other impacts)
IPCC,
Ozone ISA,
PMISA
1 Valuation estimate excludes initial hospital and/or emergency department visits.
2 Not quantified due to data availability limitations and/or because current evidence is only suggestive of causality.
4.3.3 Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use the environmental Benefits Mapping and Analysis Program—Community
Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses
attributable to photochemical modeled changes in annual mean PM2.5 and summer season
average ozone concentrations for the years 2030, 2035, and 2040 using health impact functions
(Sacks et al., 2020). A health impact function combines information regarding: the
concentration-response relationship between air quality changes and the risk of a given adverse
outcome; the population exposed to the air quality change; the baseline rate of death or disease in
that population; and, the air pollution concentration to which the population is exposed.
BenMAP quantifies counts of attributable effects using health impact functions, which
combine information regarding the: concentration-response relationship between air quality
changes and the risk of a given adverse outcome; population exposed to the air quality change;
baseline rate of death or disease in that population; and air pollution concentration to which the
population is exposed.
The following provides an example of a health impact function, in this case for PM2.5
mortality risk. We estimate counts of PIVh.s-related total deaths (ytj) during each year i among
adults aged 18 and older (a) in each county j in the contiguous U.S. (where j = 1,...,/ and ./is
the total number of counties) as:
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where moya is the baseline total mortality rate for adults aged a = 18-99 in county j in year i
stratified in 10-year age groups, fi is the risk coefficient for total mortality for adults associated
with annual average PM2.5 exposure, G/ is the annual mean PM2.5 concentration in county j in
year and Pija is the number of county adult residents aged a = 18-99 in county j in year i
stratified into 5-year age groups.38
The BenMAP-CE tool is pre-loaded with projected population from the Woods & Poole
company; cause-specific and age-stratified death rates from the Centers for Disease Control and
Prevention, projected to future years; recent-year baseline rates of hospital admissions,
emergency department visits and other morbidity outcomes from the Healthcare Cost and
Utilization Program and other sources; concentration-response parameters from the published
epidemiologic literature cited in the ISAs for fine particles and ground-level ozone; and cost of
illness or willingness to pay economic unit values for each endpoint.
To assess economic value in a damage-function framework, the changes in environmental
quality must be translated into effects on people or on the things that people value. In some
cases, the changes in environmental quality can be directly valued. In other cases, such as for
changes in ozone and PM, a health and welfare impact analysis must first be conducted to
convert air quality changes into effects that can be assigned dollar values.
We note at the outset that EPA rarely has the time or resources to perform extensive new
research to measure directly either the health outcomes or their values for regulatory analyses.
Thus, similar to work by Kiinzli et al. (2000) and co-authors and other, more recent health
impact analyses, our estimates are based on the best available methods of benefits transfer.
Benefits transfer is the science and art of adapting primary research from similar contexts to
obtain the most accurate measure of benefits for the environmental quality change under
analysis. Adjustments are made for the level of environmental quality change, the socio-
demographic and economic characteristics of the affected population, and other factors to
improve the accuracy and robustness of benefits estimates.
38 In this illustrative example, the air quality is resolved at the county level. For this RIA, we simulate air quality
concentrations at 12 km2 grids. The BenMAP-CE tool assigns the rates of baseline death and disease stored at the
county level to the 12 km2 grid cells using an area-weighted algorithm. This approach is described in greater detail
in the appendices to the BenMAP-CE user manual.
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4.3.4 Calculating the Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the
economic value of these avoided impacts. The appropriate economic value for a change in a
health effect depends on whether the health effect is viewed ex ante (before the effect has
occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air
pollution generally lower the risk of future adverse health effects by a small amount for a large
population. The appropriate economic measure is therefore ex ante WTP for changes in risk.
However, epidemiological studies generally provide estimates of the relative risks of a particular
health effect avoided due to a reduction in air pollution. A convenient way to use these data in a
consistent framework is to convert probabilities to units of avoided statistical incidences. This
measure is calculated by dividing individual WTP for a risk reduction by the related observed
change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2
in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is
$1000, then the WTP for an avoided statistical premature mortality amounts to $10 million
($1000/0.0001 change in risk). Hence, this value is population-normalized, as it accounts for the
size of the population and the percentage of that population experiencing the risk. The same type
of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not
available. In these cases, we instead use the cost of treating or mitigating the effect to
economically value the health impact. For example, for the valuation of hospital admissions we
use the avoided medical costs as an estimate of the value of avoiding the health effects causing
the admission. These cost-of-illness (COI) estimates generally (although not in every case)
understate the true value of reductions in risk of a health effect. They tend to reflect the direct
expenditures related to treatment but not the value of avoided pain and suffering from the health
effect.
4.3.5 Benefits Analysis Data Inputs
In Figure 4-1, we summarize the key data inputs to the health impact and economic
valuation estimates, which were calculated using BenMAP-CE tool version 1.5.1. (Sacks et al.,
2020). In the sections below we summarize the data sources for each of these inputs, including
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demographic projections, incidence and prevalence rates, effect coefficients, and economic
valuation.
Figure 4-1 Data Inputs and Outputs for the BenMAP-CE Tool
4.3.5.1 Demographic Data
Quantified and monetized human health impacts depend on the demographic
characteristics of the population, including age, location, and income. We use projections based
on economic forecasting models developed by Woods & Poole, Inc. (2015). The Woods & Poole
database contains county-level projections of population by age, sex, and race to 2060, relative to
a baseline using the 2010 Census data. Projections in each county are determined simultaneously
with every other county in the U.S. to consider patterns of economic growth and migration. The
sum of growth in county-level populations is constrained to equal a previously determined
national population growth, based on Bureau of Census estimates (Hollmann et al., 2000).
According to Woods & Poole, linking county-level growth projections together and constraining
the projected population to a national-level total growth avoids potential errors introduced by
forecasting each county independently (for example, the projected sum of county-level
populations cannot exceed the national total). County projections are developed in a four-stage
process:
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• First, national-level variables such as income, employment, and populations are forecasted.
• Second, employment projections are made for 179 economic areas defined by the Bureau
of Economic Analysis, using an "export-base" approach, which relies on linking industrial-
sector production of non-locally consumed production items, such as outputs from mining,
agriculture, and manufacturing with the national economy. The export-based approach
requires estimation of demand equations or calculation of historical growth rates for output
and employment by sector.
• Third, population is projected for each economic area based on net migration rates derived
from employment opportunities and following a cohort-component method based on
fertility and mortality in each area.
• Fourth, employment and population projections are repeated for counties, using the
economic region totals as bounds. The age, sex, and race distributions for each region or
county are determined by aging the population by single year by sex and race for each year
through 2060 based on historical rates of mortality, fertility, and migration.
4.3.5.2 Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse health
effects generally provide a direct estimate of the relationship of air quality changes to the relative
risk of a health effect, rather than estimating the absolute number of avoided cases. For example,
a typical result might be that a 5 |ig/m3 decrease in daily PM2.5 levels is associated with a
decrease in hospital admissions of 3 percent. A baseline incidence rate, necessary to convert this
relative change into a number of cases, is the estimate of the number of cases of the health effect
per year in the assessment location, as it corresponds to baseline pollutant levels in that location.
To derive the total baseline incidence per year, this rate must be multiplied by the corresponding
population number. For example, if the baseline incidence rate is the number of cases per year
per million people, that number must be multiplied by the millions of people in the total
population.
The Health Benefits TSD (see Table 12) summarizes the sources of baseline incidence
rates and reports average incidence rates for the endpoints included in the analysis. For both
baseline incidence and prevalence data, we used age-specific rates where available. We applied
concentration-response functions to individual age groups and then summed over the relevant
age range to provide an estimate of total population benefits. National-level incidence rates were
used for most morbidity endpoints, whereas county-level data are available for premature
mortality. Whenever possible, the national rates used are national averages, because these data
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are most applicable to a national assessment of benefits. For some studies, however, the only
available incidence information comes from the studies themselves; in these cases, incidence in
the study population is assumed to represent typical incidence at the national level.
We projected mortality rates such that future mortality rates are consistent with our
projections of population growth. To perform this calculation, we began first with an average of
2007-2016 cause-specific mortality rates. Using Census Bureau projected national-level annual
mortality rates stratified by age range, we projected these mortality rates to 2060 in 5-year
increments (U.S. Census Bureau). Further information regarding this procedure may be found in
the Health Benefits TSD and the appendices to the BenMAP user manual (U.S. EPA, 2022a).
The baseline incidence rates for hospital admissions and emergency department visits
reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update cross-
state (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial
infarction. These revised rates are more recent than the rates they replace and more accurately
represent the rates at which populations of different ages, and in different locations, visit the
hospital and emergency department for air pollution-related illnesses. Lastly, these rates reflect
unscheduled hospital admissions only, which represents a conservative assumption that most air
pollution-related visits are likely to be unscheduled. If air pollution-related hospital admissions
are scheduled, this assumption would underestimate these benefits.
4.3.5.3 Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits analysis
is described fully in the Health Benefits TSD. Because of the substantial economic value
associated with estimated counts of PIVh.s-attributable deaths, we describe our rationale for
selecting among long-term exposure epidemiologic studies below; a detailed description of all
remaining endpoints may be found in the Health Benefits TSD.
A substantial body of published scientific literature documents the association between
PM2.5 concentrations and the risk of premature death integrated (U.S. EPA, 2019a, 2022f). This
body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM
ISA, completed as part of this review of the filterable PM standards and reviewed by the Clean
Air Scientific Advisory Committee (CASAC) (U.S. EPA Science Advisory Board, 2022)
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concluded that there is a causal relationship between mortality and both long-term and short-term
exposure to PM2.5 based on the full body of scientific evidence. The size of the mortality effect
estimates from epidemiologic studies, the serious nature of the effect itself, and the high
monetary value ascribed to prolonging life make mortality risk reduction the most significant
health endpoint quantified in this analysis.
EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related
premature death, following a systematic approach detailed in the Health Benefits TSD
accompanying this RIA that is generally consistent with previous RIAs (e.g. (U.S. EPA, 2019b,
2020a, 2020b, 2021, 2022c)). Briefly, clinically significant epidemiologic studies of health
endpoints for which ISAs report strong evidence are evaluated using established minimum and
preferred criteria for identifying studies and hazard ratios best characterizing risk. Following this
systematic approach led to the identification of three studies best characterizing the risk of
premature death associated with long-term exposure to PM2.5 in the U.S. (Pope et al., 2019;
Turner et al., 2016; Wu et al., 2020). The PM ISA, Supplement to the ISA, and 2022 Policy
Assessment also identified these three studies as providing key evidence of the association
between long-term PM2.5 exposure and mortality. These studies used data from three U.S.
cohorts: (1) an analysis of Medicare beneficiaries (Medicare); (2) the American Cancer Society
(ACS); and (3) the National Health Interview Survey (NHIS). As premature mortality typically
constitutes the vast majority of monetized benefits in a PM2.5 benefits assessment, quantifying
effects using risk estimates reported from multiple long-term exposure studies using different
cohorts helps account for uncertainty in the estimated number of PM-related premature deaths.
Below we summarize the three identified studies and hazard ratios and then describe our
rationale for quantifying premature PM-attributable deaths using two of these studies.
Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and all-
cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using
Medicare claims data from 2000-2016 representing over 573 million person-years of follow up
and over 27 million deaths. This cohort included over 20 percent of the U.S. population and was,
at the time of publishing, the largest air pollution study cohort to date. The authors modeled
PM2.5 exposure at a 1 km2 grid resolution using a hybrid ensemble-based prediction model that
combined three machine learning models and relied on satellite data, land-use information,
weather variables, chemical transport model simulation outputs, and monitor data. Wu et al.,
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2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression
model, and three causal inference approaches (GPS estimation, GPS matching, and GPS
weighting). All five statistical approaches provided consistent results; we report the results of the
Cox proportional hazards model here. The authors adjusted for numerous individual-level and
community-level confounders, and sensitivity analyses suggest that the results are robust to
unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for
PM2.5 are estimated from the hazard ratio (1.066) and 95 percent confidence interval (1.058-
1.074) associated with a change in annual mean PM2.5 exposure of 10.0 |ig/m3 (Wu et al., 2020,
Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in
place of the risk estimate from (Di et al., 2017). These two epidemiologic studies share many
attributes, including the Medicare cohort and statistical model used to characterize population
exposure to PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up
period and reflects more recent PM2.5 concentrations.
Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure and all-
cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in
the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the
National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539
adults from the full cohort for whom body mass index (BMI) and smoking status data were
available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5
concentrations derived from regulatory monitoring data and constructed in a universal kriging
framework using geographic variables including land use, population, and satellite estimates.
Pope et al., 2019 assigned annual-average PM2.5 exposure from 1999-2015 to each individual by
census tract and used complex (accounting for NHIS's sample design) and simple Cox
proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio
calculated using the complex model for the sub-cohort, which controls for individual-level
covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital
status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant
model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and
95 percent confidence interval (1.08-1.15) associated with a change in annual mean PM2.5
exposure of 10.0 |ig/m3 (Pope et al., 2019, Table 2, Subcohort). This study exhibits two key
strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long
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follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort
is representative of the U.S. population, especially with respect to the distribution of individuals
by race, ethnicity, income, and education.
EPA has historically used estimated Hazard Ratios from extended analyses of the ACS
cohort (Krewski et al., 2009; Pope et al., 2002; Pope et al., 1995) to estimate PM-related risk of
premature death. More recent ACS analyses (Turner et al. 2016):
• extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),
• evaluated 669,046 participants over 12,662,562 person-years of follow up and 237,201
observed deaths, and
• applied a more advanced exposure estimation approach than had previously been used
when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy
framework with national-level land use regression models.
The total mortality hazard ratio best estimating risk from these ACS cohort studies was
based on a random-effects Cox proportional hazard model incorporating multiple individual and
ecological covariates (relative risk =1.06, 95 percent confidence intervals 1.04-1.08 per 10|ig/m3
increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk
estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable
mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the
Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range
(greater than 29 versus greater than 64), only the Wu et al., 2020 and Pope et al., 2019 are
included in the main benefits assessments, with Wu et al., 2020 representing results from both
the Medicare and ACS cohorts.
4.3.6 Quantifying Cases of Ozone-Attributable Premature Death
Mortality risk reductions account for the majority of monetized ozone-related and PM2.5-
related benefits. For this reason, this subsection and the following provide a brief background of
the scientific assessments that underly the quantification of these mortality risks and identifies
the risk studies used to quantify them in this RIA, for ozone and PM2.5 respectively. As noted
above, (U.S. EPA, 2023) describes fully the Agency's approach for quantifying the number and
value of ozone and PM2.5 air pollution-related impacts, including additional discussion of how
the Agency selected the risk studies used to quantify them in this RIA. The Health Benefits TSD
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also includes additional discussion of the assessments that support quantification of these
mortality risk than provide here.
In 2008, the National Academies of Science (NRC, 2008) issued a series of
recommendations to EPA regarding the procedure for quantifying and valuing ozone-related
mortality due to short-term exposures. Chief among these was that"... short-term exposure to
ambient ozone is likely to contribute to premature deaths" and the committee recommended that
"ozone-related mortality be included in future estimates of the health benefits of reducing ozone
exposures..." The NAS also recommended that".. .the greatest emphasis be placed on the
multicity and [National Mortality and Morbidity Air Pollution Studies (NMMAPS)] ... studies
without exclusion of the meta-analyses" (NRC, 2008). Prior to the 2015 Ozone NAAQS RIA,
the Agency estimated ozone-attributable premature deaths using an NMMAPS-based analysis of
total mortality (Bell et al., 2004), two multi-city studies of cardiopulmonary and total mortality
(Huang et al., 2005; Schwartz, 2005) and effect estimates from three meta-analyses of non-
accidental mortality (Bell et al., 2005; Ito et al., 2005; Levy et al., 2005). Beginning with the
2015 Ozone NAAQS RIA, the Agency began quantifying ozone-attributable premature deaths
using two newer multi-city studies of non-accidental mortality smith (Smith et al., 2009;
Zanobetti and Schwartz, 2008) and one long-term cohort study of respiratory mortality (Jerrett et
al. 2009). The 2020 Ozone ISA included changes to the causality relationship determinations
between short-term exposures and total mortality, as well as including more recent
epidemiologic analyses of long-term exposure effects on respiratory mortality. We estimate
counts of ozone-attributable respiratory death from short-term exposures a pooled risk estimate
calculated using parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al.
(2009).Consistent with the RIA for the Final Revised Cross-State Air Pollution Rule (CSAPR)
Update for the 2008 Ozone NAAQSRCU analysis (U.S. EPA, 2021), we use two estimates of
ozone-attributable respiratory deaths from short-term exposures are estimated using the risk
estimate parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009). Ozone-
attributable respiratory deaths from long-term exposures are estimated using Turner et al. (2016).
Due to time and resource limitations, we were unable to reflect the warm season defined by
Zanobetti and Schwartz (2008) as June-August. Instead, we apply this risk estimate to our
standard warm season of May-September.
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4.3.7 Quantifying Cases of PM2.5-A ttributable Premature Death
When quantifying PM-attributable cases of adult mortality, we use the effect coefficients
from two epidemiology studies examining two large population cohorts: the American Cancer
Society cohort (Turner et al., 2016) and the Medicare cohort (Di et al., 2017). The Integrated
Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2019a) indicates that the ACS
and Medicare cohorts provide strong evidence of an association between long-term PM2.5
exposure and premature mortality with support from additional cohort studies. There are distinct
attributes of both the ACS and Medicare cohort studies that make them well-suited to being used
in a PM benefits assessment and so here we present PM2.5 related effects derived using relative
risk estimates from both cohorts.
The PM ISA, which was reviewed by the Clean Air Scientific Advisory Committee of
EPA's Science Advisory Board (U.S. EPA Science Advisory Board, 2022), concluded that there
is a causal relationship between mortality and both long-term and short-term exposure to PM2.5
based on the entire body of scientific evidence. The PM ISA also concluded that the scientific
literature supports the use of a no-threshold log-linear model to portray the PM-mortality
concentration-response relationship while recognizing potential uncertainty about the exact
shape of the concentration-response relationship. The 2019 PM ISA, which informed the setting
of the 2020 PM NAAQS, reviewed available studies that examined the potential for a
population-level threshold to exist in the concentration-response relationship. Based on such
studies, the ISA concluded that the evidence supports the use of a "no-threshold" model and that
"little evidence was observed to suggest that a threshold exists" (U.S. EPA, 2009a). Consistent
with this evidence, the Agency historically has estimated health impacts above and below the
prevailing NAAQS (U.S. EPA, 2019b, 2021, 2022c)
4.3.8 Characterizing Uncertainty in the Estimated Benefits
In any complex analysis using estimated parameters and inputs from numerous models,
there are likely to be many sources of uncertainty. This analysis is no exception. The Health
Benefits TSD details our approach to characterizing uncertainty in both quantitative and
qualitative terms (U.S. EPA, 2023). The Health Benefits TSD describes the sources of
uncertainty associated with key input parameters including emissions inventories, air quality data
from models (with their associated parameters and inputs), population data, population estimates,
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health effect estimates from epidemiology studies, economic data for monetizing benefits, and
assumptions regarding the future state of the country (i.e., regulations, technology, and human
behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated
benefits. When the uncertainties from each stage of the analysis are compounded, even small
uncertainties can have large effects on the total quantified benefits.
To characterize uncertainty and variability into this assessment, we incorporate three
quantitative analyses described below and in greater detail within the Health Benefits TSD
(Section 7.1):
1. A Monte Carlo assessment that accounts for random sampling error and between
study variability in the epidemiological and economic valuation studies;
2. The quantification of PM-related mortality using alternative PM2.5 mortality effect
estimates drawn from two long-term cohort studies; and
3. Presentation of 95th percentile confidence interval around each risk estimate.
Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in
Section 7.1 of the Health Benefits TSD:
1. For adult all-cause mortality:
a. The distributions of air quality concentrations experienced by the original
cohort population (Health Benefits TSD Section 7.1.2.1);
b. Methods of estimating and assigning exposures in epidemiologic studies
(Health Benefits TSD Section 7.1.2.2);
c. Confounding by ozone (Health Benefits TSD Section 7.1.2.3); and
d. The statistical technique used to generate hazard ratios in the epidemiologic
study (Health Benefits TSD Section 7.1.2.4).
2. Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3),
cardiovascular hospital admissions (Health Benefits TSD Section 7.1.4,), and
respiratory hospital admissions (Health Benefits TSD Section 7.1.5);
3. Effect modification of PM2.5-attributable health effects in at-risk populations (Health
Benefits TSD Section 7.1.6).
Quantitative consideration of baseline incidence rates and economic valuation estimates
are provided in Section 7.3 and 7.4 of the Health Benefits TSD, respectively. Qualitative
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discussions of various sources of uncertainty can be found in Section 7.5 of the Health Benefits
TSD.
4.3.8.1 Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random
sampling error associated with the concentration response functions from epidemiological
studies and random effects modeling to characterize both sampling error and variability across
the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software
randomly samples from a distribution of incidence and valuation estimates to characterize the
effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to
generate confidence intervals around the estimated health impact and monetized benefits. The
reported standard errors in the epidemiological studies determined the distributions for individual
effect estimates for endpoints estimated using a single study. For endpoints estimated using a
pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and
the variance across studies. The confidence intervals around the monetized benefits incorporate
the epidemiology standard errors as well as the distribution of the valuation function. These
confidence intervals do not reflect other sources of uncertainty inherent within the estimates,
such as baseline incidence rates, populations exposed, and transferability of the effect estimate to
diverse locations. As a result, the reported confidence intervals and range of estimates give an
incomplete picture about the overall uncertainty in the benefits estimates.
4.3.8.2 Sources of Uncertainty Treated Qualitatively
Although we strive to incorporate as many quantitative assessments of uncertainty as
possible, there are several aspects we are only able to address qualitatively. These attributes are
summarized below and described more fully in the Health Benefits TSD.
Key assumptions underlying the estimates for premature mortality, which account for
over 98 percent of the total monetized benefits in this analysis, include the following:
1. We assume that all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality. This is an important assumption,
because PM2.5 varies considerably in composition across sources, but the scientific
evidence is not yet sufficient to allow differentiation of effect estimates by particle
type. The PM ISA, which was reviewed by CASAC, concluded that "across exposure
durations and health effects categories ... the evidence does not indicate that any one
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source or component is consistently more strongly related with health effects than
PM2.5 mass" (U.S. EPA Science Advisory Board, 2022).
2. We assume that the health impact function for fine particles is log-linear down to the
lowest air quality levels modeled in this analysis. Thus, the estimates include health
benefits from reducing fine particles in areas with varied concentrations of PM2.5,
including both regions that are in attainment with the fine particle standard and those
that do not meet the standard down to the lowest modeled concentrations. The PM
ISA concluded that "the majority of evidence continues to indicate a linear, no-
threshold concentration-response relationship for long-term exposure to PM2.5 and
total (nonaccidental) mortality" (U.S. EPA Science Advisory Board, 2022).
3. We assume that there is a "cessation" lag between the change in PM exposures and
the total realization of changes in mortality effects. Specifically, we assume that some
of the incidences of premature mortality related to PM2.5 exposures occur in a
distributed fashion over the 20 years following exposure based on the advice of the
board (U.S. EPA Science Advisory Board, 2004), which affects the valuation of
mortality benefits at different discount rates. Similarly, we assume there is a cessation
lag between the change in PM exposures and both the development and diagnosis of
lung cancer.
4.3.9 Estimated Number and Economic Value of Health Benefits
To directly compare benefits estimates associated with a rulemaking to cost estimates, the
number of instances of each air pollution-attributable health impact must be converted to a
monetary value. This requires a valuation estimate for each unique health endpoint, and
potentially also discounting if the benefits are expected to accrue over more than a single year, as
recommended by the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014). Below we
report the estimated number of reduced premature deaths and illnesses in each year relative to
the baseline along with the 95 percent confidence interval (Table 4-2 and Table 4-3 for ozone-
related health impacts and Table 4-4 and Table 4-5 for PM2.5-related impacts). The number of
reduced estimated deaths and illnesses from the proposed regulatory option and more stringent
regulatory alternative are calculated from the sum of individual reduced mortality and illness risk
across the population.
To directly compare benefits estimates associated with a rulemaking to cost estimates, the
number of instances of each air pollution-attributable health impact must be converted to a
monetary value. This requires a valuation estimate for each unique health endpoint, and
potentially also discounting if the benefits are expected to accrue over more than a single year, as
recommended by the U.S. EPA (2014). Table 4-6 and Table 4-7 report the estimated economic
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value of avoided premature deaths and illness in each year relative to the baseline along with the
95 percent confidence interval. We also report the stream of benefits from 2028 through 2037 for
the proposed regulatory option and the unselected more stringent regulatory alternative, using the
monetized sums of long-term ozone and PM2.5 mortality and morbidity impacts (Table 4-8 and
Table 4-9).39 Note the less stringent regulatory alternative has no quantified emissions reductions
associated with the proposed requirements for PM CEMS and the removal of startup definition
number two. As a result, there are no quantified benefits associated with this regulatory option.
Table 4-2 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Proposed Regulatory Option for 2028, 2030, and 2035 (95 percent
confidence interval) a
2028 2030 2035
Avoided premature respiratory mortalities
Long-
term
exposure
Turner et al. (2016)b
2.6
(1.8 to 3.4)
5.7
(3.9 to 7.4)
15
(10 to 19)
Short-
term
exposure
Katsouyanni et al.
(2009)b c and Zanobetti et
al. (2008)° pooled
0.12
(0.048 to 0.19)
0.26
(0.10 to 0.40)
0.66
(0.27 to 1.0)
Morbidity effects
Long-
term
exposure
Asthma onsetd
19
(16 to 22)
37
(31 to 42)
95
(82 to 110)
Allergic rhinitis
symptomsf
110
(59 to 160)
210
(110 to 310)
560
(300 to 820)
Hospital admissions—
respiratory0
0.33
(-0.087 to 0.74)
0.71
(-0.18 to 1.6)
1.9
(-0.49 to 4.2)
Short-
term
ED visits—respiratory6
6.3
(1.7 to 13)
13
(3.5 to 27)
32
(8.9 to 68)
Asthma symptoms
3,600
(-440 to 7,500)
6,900
(-850 to 14,000)
18,000
(-2,200 to 37,000)
exposure
Minor restricted-activity
days0,6
1,700
(680 to 2,700)
3,300
(1,300 to 5,100)
8,100
(3,300 to 13,000)
School absence days
1,300
(-180 to 2,700)
2,500
(-350 to 5,200)
6,500
(-910 to 14,000)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from DA24 to MDA8.
39 EPA continues to refine its approach for estimating and reporting PM-related effects at lower concentrations. The
Agency acknowledges the additional uncertainty associated with effects estimated at these lower levels and seeks to
develop quantitative approaches for reflecting this uncertainty in the estimated PM benefits.
4-27
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Table 4-3 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the More Stringent Regulatory Option for 2028, 2030, and 2035 (95 percent
confidence interval) a
2028
2030
2035
Avoided premature respiratory mortalities
Long-
term
Turner et al. (2016)b
51
40
39
exposure
(35 to 66)
(28 to 52)
(27 to 51)
Short-
term
Katsouyanni et al.
(2009)b c and Zanobetti et
2.3
1.8
1.8
exposure
al. (2008)° pooled
(0.92 to 3.6)
(0.73 to 2.9)
(0.72 to 2.8)
Morbidity effects
Long-
term
exposure
Asthma onsetd
370
(320 to 420)
270
(230 to 310)
250
(220 to 290)
Allergic rhinitis
symptomsf
2,100
(1,100 to 3,100)
1,600
(840 to 2,300)
1,500
(790 to 2,200)
Hospital admissions—
6.3
5.0
5.2
respiratory0
(-1.6 to 14)
(-1.3 to 11)
(-1.3 to 11)
Short-
term
ED visits—respiratory6
120
(33 to 250)
87
(24 to 180)
89
(24 to 190)
Asthma symptoms
69,000
(-8,500 to 140,000)
51,000
(-6,300 to 110,000)
48,000
(-5,900 to 99,000)
exposure
Minor restricted-activity
32,000
23,000
22,000
days0,6
(13,000 to 51,000)
(9,400 to 37,000)
(8,800 to 35,000)
School absence days
24,000
(-3,400 to 51,000)
18,000
(-2,600 to 38,000)
17,000
(-2,400 to 36,000)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from DA24 to MDA8.
4-28
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Table 4-4 Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and
Illnesses for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent
confidence interval)
Avoided Mortality
2028
2030
2035
(Pope et al., 2019) (adult
mortality ages 18-99 years)
11
(7.7 to 14)
8.2
(5.8 to 10)
15
(11 to 19)
(Wu et al., 2020) (adult mortality
ages 65-99 years)
5.1
(4.5 to 5.7)
4.0
(3.5 to 4.4)
7.4
(6.5 to 8.2)
(Woodruff et al., 2008) (infant
mortality)
0.013
(-0.0079 to 0.032)
0.0079
(-0.0049 to 0.020)
0.014
(-0.0087 to 0.036)
Avoided Morbidity
2028
2030
2035
Hospital admissions—
cardiovascular (age >18)
0.76
(0.55 to 0.96)
0.57
(0.41 to 0.72)
1.1
(0.78 to 1.4)
Hospital admissions—respiratory
0.42
(0.19 to 0.65)
0.27
(0.12 to 0.41)
0.48
(0.21 to 0.74)
ED visits-cardiovascular
1.6
(-0.61 to 3.7)
1.1
(-0.44 to 2.7)
2.2
(-0.84 to 5.1)
ED visits—respiratory
3.1
(0.61 to 6.4)
2.2
(0.44 to 4.7)
4.2
(0.82 to 8.7)
Acute Myocardial Infarction
0.17
(0.10 to 0.25)
0.13
(0.073 to 0.18)
0.24
(0.14 to 0.33)
Cardiac arrest
0.082
(-0.033 to 0.19)
0.059
(-0.024 to 0.13)
0.11
(-0.044 to 0.24)
Hospital admissions-
Alzheimer's Disease
2.6
(1.9 to 3.2)
1.7
(1.3 to 2.1)
3.8
(2.9 to 4.8)
Hospital admissions-
Parkinson's Disease
0.35
(0.18 to 0.51)
0.28
(0.14 to 0.41)
0.49
(0.25 to 0.72)
Stroke
0.32
(0.084 to 0.55)
0.24
(0.062 to 0.41)
0.44
(0.11 to 0.76)
Lung cancer
0.37
(0.11 to 0.61)
0.27
(0.082 to 0.45)
0.52
(0.16 to 0.87)
Hay Fever/Rhinitis
82
(20 to 140)
55
(13 to 95)
100
(24 to 170)
Asthma Onset
13
(12 to 13)
8.4
(8.1 to 8.8)
15
(15 to 16)
Asthma symptoms - Albuterol
use
2,400
(-1,200 to 5,800)
1,600
(-780 to 3,900)
2,900
(-1,400 to 7,200)
Lost work days
630
(530 to 720)
420
(360 to 490)
770
(650 to 880)
Minor restricted-activity days
3,700
(3,000 to 4,400)
2,500
(2,000 to 2,900)
4,500
(3,700 to 5,300)
Note: Values rounded to two significant figures.
4-29
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Table 4-5 Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and
Illnesses for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent
confidence interval)a,b
Avoided Mortality
2028
2030
2035
(Pope et al., 2019) (adult
240
38
96
mortality ages 18-99 years)
(170 to 300)
(27 to 48)
(69 to 120)
(Wu et al., 2020) (adult mortality
110
19
47
ages 65-99 years)
(100 to 130)
(16 to 21)
(41 to 52)
(Woodruff et al., 2008) (infant
0.24
0.031
0.10
mortality)
(-0.15 to 0.63)
(-0.019 to 0.080)
(-0.064 to 0.26)
Avoided Morbidity
2028
2030
2035
Hospital admissions—
18
2.7
6.9
cardiovascular (age >18)
(13 to 22)
(2.0 to 3.5)
(5.0 to 8.7)
Hospital admissions—respiratory
8.0
0.83
3.5
(3.5 to 12)
(0.36 to 1.3)
(1.5 to 5.4)
ED visits-cardiovascular
35
5.8
14
(-14 to 83)
(-2.2 to 13)
(-5.5 to 34)
ED visits—respiratory
68
10
27
(13 to 140)
(2.0 to 22)
(5.4 to 57)
Acute Myocardial Infarction
3.9
0.55
1.6
(2.3 to 5.5)
(0.32 to 0.77)
(0.93 to 2.2)
Cardiac arrest
1.8
0.27
0.69
(-0.72 to 4.0)
(-0.11 to 0.62)
(-0.28 to 1.6)
Hospital admissions-
56
2.0
26
Alzheimer's Disease
(42 to 70)
(1.5 to 2.5)
(19 to 32)
Hospital admissions-
7.7
1.2
3.0
Parkinson's Disease
(3.9 to 11)
(0.61 to 1.8)
(1.5 to 4.4)
Stroke
7.5
1.2
2.8
(1.9 to 13)
(0.30 to 2.0)
(0.73 to 4.8)
Lung cancer
8.2
1.3
3.3
(2.5 to 14)
(0.40 to 2.2)
(1.0 to 5.5)
Hay Fever/Rhinitis
1,500
220
670
(360 to 2,600)
(54 to 390)
(160 to 1,200)
Asthma Onset
230
35
100
(220 to 240)
(33 to 36)
(98 to 110)
Asthma symptoms - Albuterol
43,000
6,600
20,000
use
(-21,000 to 100,000)
(-3,200 to 16,000)
(-9,600 to 48,000)
Lost work days
12,000
1,800
5,000
(10,000 to 14,000)
(1,600 to 2,100)
(4,200 to 5,800)
Minor restricted-activity daysd f
70,000
11,000
30,000
(57,000 to 83,000)
(8,800 to 13,000)
(24,000 to 35,000)
a Values rounded to two significant figures.
b We estimated ozone benefits for changes in NOx for the ozone season and changes in PM2 5 and PM2 5 precursors
forEGUs in 2026.
0 Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
d Converted ozone risk estimate metric from MDA1 to MDA8.
e Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
f Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
g Converted ozone risk estimate metric from DA24 to MDA8.
4-30
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Table 4-6 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Proposed Regulatory Option in 2028,
2030, and 2035 (95 percent confidence int
erval; millions of 2019 dollars)"'
Disc.
Rate
Pollutant
2028
2030
2035
3%
Ozone
Benefits
$4 $30
($1 to and ($3 to
$8) $78)
$7 $64
($2 to $16) and ^70)
$170
($5 to and <*18
$41> $450)
PM25
Benefits
$55 $110
($6 to and ($11 to
$140) $300)
$43 "*8^
($4 to $110) °nd ($$283^
$16°
($8 to and
$21°) $430)
Ozone
plus PM2 5
Benefits
$59 $140
($7 to and ($14 to
$150)° $380)d
$50 $150
($6 to and ($15 to
$130)° $400)d
$330
$100 -j ($33
($13 to $250)° to
$880)d
7%
Ozone
Benefits
$3 $27
($1 to and ($3 to
$7) $70)
$7 $58
($1 to $15) and
^7 $150
($3 to and <*15
$39> $400)
PM25
Benefits
$49 $100
($5 to and ($10 to
$130) $270)
$39
($4 to $100) and
*73 $15°
($7 to and <*14
$19°) $390)
Ozone
plus PM2 5
Benefits
$52 $130
($6 to and ($12 to
$140)° $340)d
$46 $140
($5 to and ($13 to
$120)° $360)d
$300
$90 -j ($29
nnn
($10 to $230)° to
$790)d
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b We estimated changes in NOx for the ozone season and changes in PM2 5 and PM2 5 precursors in 2028, 2030, and
2035.
0 Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
d Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-31
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Table 4-7 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the More Stringent Regulatory Option in
2028, 2030, and 2035 (95 percent confidence interval; millions of 2
019 dollars)a b
Disc.
Rate
Pollutant
2028
2030
2035
3%
Ozone
Benefits
$69 $570
($17 to and ($62 to
$150) $1,500)
$53 $460
($13 to and ($48 to
$110) $1,200)
$51 $460
($12 to and ($48 to
$110) $1,200)
PM25
Benefits
$1,200 $2,500
($120 to and ($240 to
$3,200) $6,700)
$200 $410
($20 to and ($38 to
$520) $1,100)
$520 $1,100
($53 to and ($99 to
$1,300) $2,800)
Ozone
plus
PM25
Benefits
$1,300 $3,100
($140 to and ($300 to
$3,400)° $8,200)d
$250 $870
($33 to and ($86 to
$630)° $2,300)d
$570 $1,600
($65 to and ($150 to
$1,400)° $4,000)d
7%
Ozone
Benefits
$62 $510
($11 to and ($51 to
$140) $1,300)
$48 $410
($8 to and ($40 to
$110) $1,100)
$46 $410
($8 to and ($40 to
$110) $1,100)
PM25
Benefits
$1,100 $2,300
($110 to and ($210 to
$2,900) $6,000)
$180 $370
($18 to and ($34 to
$470) $970)
$470 $950
($46 to and ($88 to
$1,200) $2,500)
Ozone
plus
PM25
Benefits
$1,200 $2,800
($120 to and ($260 to
$3,000)° $7,300)d
$230 $780
($26 to and ($74 to
$580)° $2,100)d
$520 $1,400
($54 to and ($130 to
$1,300)° $3,600)d
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b We estimated changes in NOx for the ozone season and changes in PM2 5 and PM2 5 precursors in 2028, 2030, and
2035.
0 Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
d Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-32
-------
Table 4-8 Stream of Estimated Human Health Benefits from 2028 through 2037:
Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term
PM2.5 Mortality (discounted at 3 percent; millions of 2019 dollars)3
Year
Proposed
Regulatory Option
More Stringent
Regulatory Option
2028*
$140
$3,100
2029
$150
$840
2030*
$150
$860
2031
$160
$890
2032
$310
$1,400
2033
$320
$1,400
2034
$320
$1,500
2035*
$330
$1,500
2036
$340
$1,600
2037
$350
$1,600
Present Value
$1,900
$11,000
Equivalent Annualized Value
$220
$1,300
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-
based air quality estimates. Benefits calculated as value of avoided: PM2 5-attributable deaths (quantified using a
concentration-response relationship from the Wu et al. 2020 study and the Pope et al. 2019 study); Ozone-
attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and
PM2 5 and ozone-related morbidity effects.
a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of
standards will not be complete until 2028.
Table 4-9 Stream of Estimated Human Health Benefits from 2028 through 2037:
Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term
PM2.5 Mortality (discounted at 7 percent; millions of 2019 dollars)3
Year
Proposed Regulatory Option
More Stringent Regulatory
Option
2028*
$130
$2,800
2029
$130
$750
2030*
$140
$770
203 1
$140
$800
2032
$270
$1,200
2033
$280
$1,300
2034
$290
$1,300
2035*
$300
$1,400
2036
$310
$1,400
2037
$310
$1,400
Present Value
$1,200
$7,100
Equivalent Annualized Value
$170
$1,000
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-
based air quality estimates. Benefits calculated as value of avoided: PM2 5-attributable deaths (quantified using a
concentration-response relationship from the Wu et al. 2020 study and the Pope et al. 2019 study); Ozone-
attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and
PM2 5 and ozone-related morbidity effects.
4-33
-------
a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of
standards will not be complete until 2028.
4.3.10 Additional Unquantified Criteria Pollutant Benefits
Data, time, and resource limitations prevented EPA from quantifying the estimated health
impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2
(independent of the role NO2 and SO2 play as precursors to PM2.5 and ozone), as well as
ecosystem effects, and visibility impairment due to the absence of air quality modeling data for
these pollutants in this analysis. While all health benefits and welfare benefits were not able to be
quantified, it does not imply that there are not additional benefits associated with reductions in
exposures to ozone, PM2.5, NO2 or SO2. In this section, we provide a qualitative description of
these and water quality benefits, which are listed in Table 4-10. Criteria pollutants from U.S.
EGUs can also be transported downwind into foreign countries, in particular Canada and
Mexico. Therefore, reduced criteria pollutants from U.S. EGUs can lead to public health and
welfare benefits in foreign countries. EPA is currently unable to quantify or monetize these
effects.
4-34
-------
Table 4-10 Additional Unquantified Benefit Categories
Category
Effect
Effect
Quantified
Effect More
Monetized Information
Improved Human
Health
Asthma hospital admissions
—
— NO2 ISA1
Chronic lung disease hospital admissions
—
— NO2 ISA1
Reduced incidence of
morbidity from exposure
Respiratory emergency department visits
—
— NO2 ISA1
Asthma exacerbation
—
— NO2 ISA1
to NO2
Acute respiratory symptoms
—
— NO2 ISA1
Premature mortality
—
— NO2 ISA1'2'3
Other respiratory effects (e.g., airway
hyperresponsiveness and inflammation, lung
function, other ages and populations)
—
— NO2 ISA2'3
Reduced incidence of
mortality and morbidity
through drinking water
from reduced effluent
discharges.
Bladder, colon, and rectal cancer from
halogenated disinfection byproducts exposure.
—
SEELG
BCA4
Reproductive and developmental effects from
halogenated disinfection byproducts exposure.
—
SEELG
BCA4
Neurological and cognitive effects to children
from lead exposure from fish consumption
(including need for specialized education).
—
SEELG
BCA4
Possible cardiovascular disease from lead
exposure
—
SEELG
BCA4
Reduced incidence of
morbidity and mortality
from toxics through fish
consumption from
reduced effluent
discharges.
Neurological and cognitive effects from in in-
utero mercury exposure from maternal fish
consumption
Skin and gastrointestinal cancer incidence from
arsenic exposure
—
SEELG
BCA4
SEELG
BCA4
Cancer and non-cancer incidence from exposure
to toxic pollutants (lead, cadmium, thallium,
hexavalent chromium etc.
.
SEELG
BCA4
Neurological, alopecia, gastrointestinal effects,
reproductive and developmental damage from
short-term thallium exposure.
Reduced incidence of
morbidity and mortality
from recreational water
exposure from reduced
effluent discharges.
Cancer and Non-Cancer incidence from
exposure to toxic pollutants (methylmercury,
selenium, and thallium.)
—
SEELG
BCA4
Improved Environment
Reduced visibility
impairment
Visibility in Class 1 areas
Visibility in residential areas
—
— PM ISA1
— PM ISA1
4-35
-------
Table 4-10 Additional Unquantified Benefit Categories
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Reduced effects on
materials
Household soiling
Materials damage (e.g., corrosion, increased
wear)
—
—
PMISA1,2
PM ISA2
Reduced effects from
PM deposition (metals
and organics)
Effects on individual organisms and ecosystems
—
—
PMISA2
Visible foliar injury on vegetation
—
—
Ozone ISA1
Reduced vegetation growth and reproduction
—
—
Ozone ISA1
Yield and quality of commercial forest products
and crops
—
—
Ozone ISA1
Damage to urban ornamental plants
—
—
Ozone ISA2
Reduced vegetation and
ecosystem effects from
exposure to ozone
Carbon sequestration in terrestrial ecosystems
—
—
Ozone ISA1
Recreational demand associated with forest
aesthetics
—
—
Ozone ISA2
Other non-use effects
Ozone ISA2
Ecosystem functions (e.g., water cycling,
biogeochemical cycles, net primary productivity,
leaf-gas exchange, community composition)
—
—
Ozone ISA2
Recreational fishing
—
—
NOx SOx
ISA1
Tree mortality and decline
—
—
NOx SOx
ISA2
Reduced effects from
acid deposition
Commercial fishing and forestry effects
—
—
NOx SOx
ISA2
Recreational demand in terrestrial and aquatic
ecosystems
—
—
NOx SOx
ISA2
Other non-use effects
NOx SOx
ISA2
Ecosystem functions (e.g., biogeochemical
cycles)
—
—
NOx SOx
ISA2
Species composition and biodiversity in
terrestrial and estuarine ecosystems
—
—
NOx SOx
ISA2
Coastal eutrophication
—
—
NOx SOx
ISA2
Reduced effects from
nutrient enrichment from
deposition.
Recreational demand in terrestrial and estuarine
ecosystems
Other non-use effects
—
—
NOx SOx
ISA2
NOx SOx
ISA2
Ecosystem functions (e.g., biogeochemical
cycles, fire regulation)
—
—
NOx SOx
ISA2
4-36
-------
Table 4-10 Additional Unquantified Benefit Categories
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Reduced vegetation
effects from ambient
exposure to SO2 andNOx
Injury to vegetation from SO2 exposure
—
—
NOx SOx
ISA2
Injury to vegetation from NOx exposure
—
—
NOx SOx
ISA2
Improved water
aesthetics from reduced
effluent discharges.
Improvements in water clarity, color, odor in
residential, commercial, and recreational
settings.
—
—
SE ELG
BCA4
Protection of Threatened and Endangered (T&E)
species from changes in habitat and potential
population effects.
—
—
SE ELG
BCA4
Other non-use effects
—
—
SE ELG
BCA4
Effects on aquatic
organisms and other
wildlife from reduced
effluent discharges
Changes in sediment contamination on benthic
communities and potential for re-entrainment.
—
—
SE ELG
BCA4
Quality of recreational fishing and other
recreational use values.
—
—
SE ELG
BCA4
Commercial fishing yields and harvest quality.
—
—
SE ELG
BCA4
Reduced water treatment
costs from reduced
effluent discharges
Reduced drinking, irrigation, and other
agricultural use water treatment costs.
—
—
SE ELG
BCA4
Increased storage availability in reservoirs
—
—
SE ELG
BCA4
Reduced sedimentation
from effluent discharges
Improved functionality of navigable waterways
—
—
SE ELG
BCA4
Decreased cost of dredging
—
—
SE ELG
BCA4
Benefits from effects aquatic and riparian
species from additional water availability.
—
—
SE ELG
BCA4
Benefits of reduced
water withdrawal
Increased water availability in reservoirs
increasing hydropower supply, recreation, and
other services.
—
—
SE ELG
BCA4
1 We assess these benefits qualitatively due to data and resource limitations for this RIA.
2 We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
3 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant
concerns over the strength of the association.
4 Benefit and Cost Analysis (BCA) for Revisions to the Effluent Limitations Guidelines (ELG) and Standards for the Steam
Electric (SE) Power Generating Point Source Category.
4.3.10.1 NO2 Health Benefits
In addition to being a precursor to PM2.5 and ozone, NOx emissions are also linked to a
variety of adverse health effects associated with direct exposure. We were unable to estimate the
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health benefits associated with reduced NO2 exposure in this analysis. Following a
comprehensive review of health evidence from epidemiologic and laboratory studies, the
Integrated Science Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) (U.S. EPA,
2016) concluded that there is a likely causal relationship between respiratory health effects and
short-term exposure to NO2. These epidemiologic and experimental studies encompass a number
of endpoints including emergency department visits and hospitalizations, respiratory symptoms,
airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also
concluded that the relationship between short-term NO2 exposure and premature mortality was
"suggestive but not sufficient to infer a causal relationship," because it is difficult to attribute the
mortality risk effects to NO2 alone. Although the NOx ISA stated that studies consistently
reported a relationship between NO2 exposure and mortality, the effect was generally smaller
than that for other pollutants such as PM.
4.3.10.2 SO2 Health Benefits
In addition to being a precursor to PM2.5, SO2 emissions are also linked to a variety of
adverse health effects associated with direct exposure. We were unable to estimate the health
benefits associated with reduced SO2 in this analysis. Therefore, this analysis only quantifies and
monetizes the PM2.5 benefits associated with the reductions in SO2 emissions. Following an
extensive evaluation of health evidence from epidemiologic and laboratory studies, the
Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) concluded that
there is a causal relationship between respiratory health effects and short-term exposure to SO2
sulfur (U.S. EPA, 2017). The immediate effect of SO2 on the respiratory system in humans is
bronchoconstriction. Asthmatics are more sensitive to the effects of SO2, likely resulting from
preexisting inflammation associated with this disease. A clear concentration-response
relationship has been demonstrated in laboratory studies following exposures to SO2 at
concentrations between 20 and 100 parts per billion (ppb), both in terms of increasing severity of
effect and percentage of asthmatics adversely affected. Based on our review of this information,
we identified three short-term morbidity endpoints that the SO2 ISA identified as a "causal
relationship": asthma exacerbation, respiratory-related emergency department visits, and
respiratory-related hospitalizations. The differing evidence and associated strength of the
evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also
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concluded that the relationship between short-term SO2 exposure and premature mortality was
"suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to
SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a
relationship between SO2 exposure and mortality, there was a lack of robustness of the observed
associations to adjustment for other pollutants.
4.3.10.3 Ozone Welfare Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem
effects in the published literature ecological (U.S. EPA, 2020c). Sensitivity to ozone is highly
variable across species, with over 65 plant species identified as "ozone-sensitive," many of
which occur in state and national parks and forests. These effects include those that damage or
impair the intended use of the plant or ecosystem. Such effects can include reduced growth
and/or biomass production in sensitive plant species, including forest trees, reduced yield and
quality of crops, visible foliar injury, species composition shift, and changes in ecosystems and
associated ecosystem services. See Section F of the Ozone Transport Policy Analysis Proposed
Rule TSD (U.S. EPA, 2022g) for a summary of an assessment of risk of ozone-related growth
impacts on selected forest tree species.
4.3.10.4 NO2 and SO2 Welfare Benefits
As described in the Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides
of Sulfur and Particulate Matter Ecological Criteria (U.S. EPA, 2020c), NOx and SO2 emissions
also contribute to a variety of adverse welfare effects, including those associated with acidic
deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen and sulfur
causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro
invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red
spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecosystems. In the
northeastern U.S., the surface waters affected by acidification are a source of food for some
recreational and subsistence fishermen and for other consumers and support several cultural
services, including aesthetic and educational services and recreational fishing. Biological effects
of acidification in terrestrial ecosystems are generally linked to aluminum toxicity, which can
cause reduced root growth, restricting the ability of the plant to take up water and nutrients.
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These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as
droughts, cold temperatures, insect pests, and disease, leading to increased mortality of canopy
trees. Terrestrial acidification affects several important ecological services, including declines in
habitat for threatened and endangered species (cultural), declines in forest aesthetics (cultural),
declines in forest productivity (provisioning), and increases in forest soil erosion and reductions
in water retention (cultural and regulating).
Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment.
In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of
estuaries can disrupt an important source of food production, particularly fish and shellfish
production, and a variety of cultural ecosystem services, including water-based recreational and
aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and
number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets
the balance between native and nonnative plants, changing the ability of an area to support
biodiversity. When the composition of species changes, then fire frequency and intensity can
also change, as nonnative grasses fuel more frequent and more intense wildfires.
4.3.10.5 Visibility Impairment Benefits
Reducing secondary formation of PM2.5 would improve levels of visibility in the U.S.
because suspended particles and gases degrade visibility by scattering and absorbing light (U.S.
EPA 2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates,
organic carbon, elemental carbon, and soil. Visibility has direct significance to people's
enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the
quality of life where individuals live and work, and where they engage in recreational activities.
Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate
nitrate is an important contributor to light extinction in California and the upper Midwestern
U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2012) show
that visibility benefits can be a significant welfare benefit category. Without air quality
modeling, we are unable to estimate visibility-related benefits, and we are also unable to
determine whether the emission reductions associated with this proposed rule would be likely to
have a significant impact on visibility in urban areas or Class I areas.
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Reductions in emissions of NO2 will improve the level of visibility throughout the U.S.
because these gases (and the particles of nitrate and sulfate formed from these gases) impair
visibility by scattering and absorbing light (U.S. EPA, 2009b). Visibility is also referred to as
visual air quality (VAQ), and it directly affects people's enjoyment of a variety of daily activities
(U.S. EPA, 2009b). Good visibility increases quality of life where individuals live and work, and
where they travel for recreational activities, including sites of unique public value, such as the
Great Smoky Mountains National Park (U.S. EPA, 2009b).
4.4 Climate Pollutant Benefits
We estimate the climate benefits from this proposed rule using estimates of the social
cost of greenhouse gases (SC-GHG), specifically the SC-CO2. The SC-CO2 is the monetary
value of the net harm to society associated with a marginal increase in CO2 emissions in a given
year, or the benefit of avoiding that increase. In principle, SC-CO2 includes the value of all
climate change impacts (both negative and positive), including (but not limited to) changes in net
agricultural productivity, human health effects, property damage from increased flood risk
natural disasters, disruption of energy systems, risk of conflict, environmental migration, and the
value of ecosystem services. The SC-CO2, therefore, reflects the societal value of reducing
emissions of the gas in question by one metric ton and is the theoretically appropriate value to
use in conducting benefit-cost analyses of policies that affect CO2 emissions. In practice, data
and modeling limitations naturally restrain the ability of SC-CO2 estimates to include all the
important physical, ecological, and economic impacts of climate change, such that the estimates
are a partial accounting of climate change impacts and will therefore, tend to be underestimates
of the marginal benefits of abatement. The EPA and other Federal agencies began regularly
incorporating SC-CO2 estimates in their benefit-cost analyses conducted under E.O. 128 6640
40 Presidents since the 1970s have issued executive orders requiring agencies to conduct analysis of the economic
consequences of regulations as part of the rulemaking development process. E.O. 12866, released in 1993 and still in
effect today, requires that for all economically significant regulatory actions, an agency provide an assessment of the
potential costs and benefits of the regulatory action, and that this assessment include a quantification of benefits and
costs to the extent feasible. Many statutes also require agencies to conduct at least some of the same analyses
required under E.O. 12866, such as the Energy Policy and Conservation Act which mandates the setting of fuel
economy regulations. For purposes of this action, monetized climate benefits are presented for purposes of providing
a complete benefit-cost economic impact analysis under E.O. 12866 and other relevant executive orders. The
estimates of change in GHG emissions and the monetized benefits associated with those changes play no part in the
record basis for this action.
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since 2008, following a Ninth Circuit Court of Appeals remand of a rule for failing to monetize
the benefits of reducing CO2 emissions in that rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a
report that provides a roadmap for how to update SC-GHG estimates used in Federal analyses
going forward to ensure that they reflect advances in the scientific literature (National
Academies, 2017). The National Academies' report recommended specific criteria for future SC-
GHG updates, a modeling framework to satisfy the specified criteria, and both near-term updates
and longer-term research needs pertaining to various components of the estimation process. The
research community has made considerable progress in developing new data and methods that
help to advance various components of the SC-GHG estimation process in response to the
National Academies' recommendations.
In a first-day executive order (E.O. 13990), Protecting Public Health and the
Environment and Restoring Science to Tackle the Climate Crisis, President Biden called for a
renewed focus on updating estimates of the SC-GHG to reflect the latest science, noting that "it
is essential that agencies capture the full benefits of reducing greenhouse gas emissions as
accurately as possible." Important steps have been taken to begin to fulfill this directive of E.O.
13990. In February 2021, the Interagency Working Group on the SC-GHG (IWG) released a
technical support document (hereinafter the "February 2021 SC-GHG TSD") that provided a set
of IWG recommended SC-GHG estimates while work on a more comprehensive update is
underway to reflect recent scientific advances relevant to SC-GHG estimation (IWG 2021). In
addition, as discussed further below, EPA has developed a draft updated SC-GHG methodology
within a sensitivity analysis in the regulatory impact analysis of EPA's November 2022
supplemental proposal for oil and gas standards that is currently undergoing external peer review
and a public comment process.41
The EPA has applied the IWG's recommended interim SC-GHG estimates in the
Agency's regulatory benefit-cost analyses published since the release of the February 2021 TSD
and is likewise using them in this RIA. We have evaluated the SC-GHG estimates in the
February 2021 TSD and have determined that these estimates are appropriate for use in
estimating the social benefits of GHG reductions expected to occur as a result of the proposed
41 See https://www.epa.gov/environmental-economics/scghg
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and alternative standards. These SC-GHG estimates are interim values developed for use in
benefit-cost analyses until updated estimates of the impacts of climate change can be developed
based on the best available science and economics. After considering the TSD, and the issues
and studies discussed therein, the EPA concludes that these estimates, while likely an
underestimate, are the best currently available SC-GHG estimates until revised estimates have
been developed reflecting the latest, peer-reviewed science.
The SC-GHG estimates presented in the February 2021 SC-GHG TSD and used in this
RIA were developed over many years, using a transparent process, peer-reviewed
methodologies, the best science available at the time of that process, and with input from the
public. Specifically, in 2009, an interagency working group (IWG) that included the EPA and
other executive branch agencies and offices was established to develop estimates relying on the
best available science for agencies to use. The IWG published SC-CO2 estimates in 2010 that
were developed from an ensemble of three widely cited integrated assessment models (IAMs)
that estimate global climate damages using highly aggregated representations of climate
processes and the global economy combined into a single modeling framework. The three IAMs
were run using a common set of input assumptions in each model for future population,
economic, and CO2 emissions growth, as well as equilibrium climate sensitivity (ECS) - a
measure of the globally averaged temperature response to increased atmospheric CO2
concentrations. These estimates were updated in 2013 based on new versions of each IAM.42 In
August 2016 the IWG published estimates of the social cost of methane (SC-CH4) and nitrous
oxide (SC-N2O) using methodologies that are consistent with the methodology underlying the
SC-CO2 estimates. The modeling approach that extends the IWG SC-CO2 methodology to non-
CO2 GHGs has undergone multiple stages of peer review. The SC-CH4 and SC-N2O estimates
were developed by Marten et al. (2015) and underwent a standard double-blind peer review
process prior to journal publication. These estimates were applied in RIAs of EPA proposed
rulemakings with CH4 and N2O emissions impacts.43 The EPA also sought additional external
peer review of technical issues associated with its application to regulatory analysis. Following
42 Dynamic Integrated Climate and Economy (DICE) 2010 (Nordhaus, 2010), Climate Framework for Uncertainty,
Negotiation, and Distribution (FUND) 3.8 (Anthoff and Tol, 2013a, 2013b), and Policy Analysis of the Greenhouse
Gas Effect (PAGE) 2009 (Hope, 2013).
43 The SC-CH4 and SC-N20 estimates were first used in sensitivity analysis for the Proposed Rulemaking for
Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles-
Phase 2 (U.S. EPA, 2015a).
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the completion of the independent external peer review of the application of the Marten et al.
(2015) estimates, the EPA began using the estimates in the primary benefit-cost analysis
calculations and tables for a number of proposed rulemakings (U.S. EPA, 2015b, 2015d). The
EPA considered and responded to public comments received for the proposed rulemakings
before using the estimates in final regulatory analyses in 2016.44 In 2015, as part of the response
to public comments received to a 2013 solicitation for comments on the SC-CO2 estimates, the
IWG announced a National Academies of Sciences, Engineering, and Medicine review of the
SC-CO2 estimates to offer advice on how to approach future updates to ensure that the estimates
continue to reflect the best available science and methodologies. In January 2017, the National
Academies released their final report, Valuing Climate Damages: Updating Estimation of the
Social Cost of Carbon Dioxide (National Academies, 2017), and recommended specific criteria
for future updates to the SC-CO2 estimates, a modeling framework to satisfy the specified
criteria, and both near-term updates and longer-term research needs pertaining to various
components of the estimation process (National Academies 2017). Shortly thereafter, in March
2017, President Trump issued E.O. 13783, which disbanded the IWG, withdrew the previous SC-
GHG TSDs, and directed agencies to ensure SC-GHG estimates used in regulatory analyses are
consistent with the guidance contained in OMB's Circular A-4, "including with respect to the
consideration of domestic versus international impacts and the consideration of appropriate
discount rates" (E.O. 13783, Section 5(c)). Benefit-cost analyses following E.O. 13783 used SC-
CO2 estimates that attempted to focus on the specific share of climate change damages in the
U.S. as captured by the models (which did not reflect many pathways by which climate impacts
affect the welfare of U.S. citizens and residents) and were calculated using two default discount
rates recommended by Circular A-4, 3 percent and 7 percent.45 All other methodological
44 See IWG (2016b) for more discussion of the SC-CH4 and SC-N20 and the peer review and public comment
processes accompanying their development.
45 The EPA regulatory analyses under E.O. 13783 included sensitivity analyses based on global SC-GHG values and
using a lower discount rate of 2.5 percent. OMB Circular A-4 (OMB, 2003) recognizes that special considerations
arise when applying discount rates if intergenerational effects are important. In the IWG's 2015 Response to
Comments, OMB—as a co-chair of the IWG—made clear that "Circular A-4 is a living document," that "the use of
7 percent is not considered appropriate for intergenerational discounting," and that "[t]here is wide support for this
view in the academic literature, and it is recognized in Circular A-4 itself." OMB, as part of the IWG, similarly
repeatedly confirmed that "a focus on global SCC estimates in [regulatory impact analyses] is appropriate" (IWG
2015).
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decisions and model versions used in SC- CO2 calculations remained the same as those used by
the IWG in 2010 and 2013, respectively.
On January 20, 2021, President Biden issued E.O. 13990, which re-established an IWG
and directed it to develop an update of the SC-CO2 estimates that reflect the best available
science and the recommendations of the National Academies. In February 2021, the IWG
recommended the interim use of the most recent SC- CO2 estimates developed by the IWG prior
to the group being disbanded in 2017, adjusted for inflation (IWG, 2021) (IWG, 2021). As
discussed in the February 2021 SC-GHG TSD, the IWG's selection of these interim estimates
reflected the immediate need to have SC- CO2 estimates available for agencies to use in
regulatory benefit-cost analyses and other applications that were developed using a transparent
process, peer reviewed methodologies, and the science available at the time of that process.
As noted above, the EPA participated in the IWG but has also independently evaluated
the interim SC-CO2 estimates published in the February 2021 SC-GHG TSD and determined
they are appropriate to use to estimate climate benefits for this action. The EPA and other
agencies intend to undertake a fuller update of the SC- CO2 estimates that takes into
consideration the advice of the National Academies (2017) and other recent scientific literature.
The EPA has also evaluated the supporting rationale of the February 2021 SC-GHG TSD,
including the studies and methodological issues discussed therein, and concludes that it agrees
with the rationale for these estimates presented in the SC-GHG TSD and summarized below.
In particular, the IWG found that the SC-CO2 estimates used under E.O. 13783 fail to
reflect the full impact of GHG emissions in multiple ways. First, the IWG concluded that those
estimates fail to capture many climate impacts that can affect the welfare of U.S. citizens and
residents. Examples of affected interests include direct effects on U.S. citizens and assets located
abroad, international trade, and tourism, and spillover pathways such as economic and political
destabilization and global migration that can lead to adverse impacts on U.S. national security,
public health, and humanitarian concerns. Those impacts are better captured within global
measures of the SC-GHGs.
In addition, assessing the benefits of U.S. GHG mitigation activities requires
consideration of how those actions may affect mitigation activities by other countries, as those
international mitigation actions will provide a benefit to U.S. citizens and residents by mitigating
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climate impacts that affect U.S. citizens and residents. A wide range of scientific and economic
experts have emphasized the issue of reciprocity as support for considering global damages of
GHG emissions. Using a global estimate of damages in U.S. analyses of regulatory actions
allows the U.S. to continue to actively encourage other nations, including emerging major
economies, to take significant steps to reduce emissions. The only way to achieve an efficient
allocation of resources for emissions reduction on a global basis—and so benefit the U.S. and its
citizens—is for all countries to base their policies on global estimates of damages.
As a member of the IWG involved in the development of the February 2021 SC-GHG
TSD, the EPA agrees with this assessment and, therefore, in this proposed rule the EPA centers
attention on a global measure of SC-CO2. This approach is the same as that taken in EPA
regulatory analyses over 2009 through 2016. A robust estimate of climate damages only to U.S.
citizens and residents that accounts for the myriad of ways that global climate change reduces the
net welfare of U.S. populations does not currently exist in the literature. As explained in the
February 2021 SC-GHG TSD, existing estimates are both incomplete and an underestimate of
total damages that accrue to the citizens and residents of the U.S. because they do not fully
capture the regional interactions and spillovers discussed above, nor do they include all of the
important physical, ecological, and economic impacts of climate change recognized in the
climate change literature, as discussed further below. The EPA, as a member of the IWG, will
continue to review developments in the literature, including more robust methodologies for
estimating the magnitude of the various damages to U.S. populations from climate impacts and
reciprocal international mitigation activities, and explore ways to better inform the public of the
full range of carbon impacts.
Second, the IWG concluded that the use of the social rate of return on capital (7 percent
under current OMB Circular A-4 guidance) to discount the future benefits of reducing GHG
emissions inappropriately underestimates the impacts of climate change for the purposes of
estimating the SC-CO2. Consistent with the findings of the National Academies (2017) and the
economic literature, the IWG continued to conclude that the consumption rate of interest is the
theoretically appropriate discount rate in an intergenerational context (IWG, 2016b) (IWG, 2010,
2013, 2016a) and recommended that discount rate uncertainty and relevant aspects of
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intergenerational ethical considerations be accounted for in selecting future discount rates.46
Furthermore, the damage estimates developed for use in the SC-GHG are estimated in
consumption-equivalent terms, and so an application of OMB Circular A-4's guidance for
regulatory analysis would then use the consumption discount rate to calculate the SC-GHG. The
EPA agrees with this assessment and will continue to follow developments in the literature
pertaining to this issue. The EPA also notes that while OMB Circular A-4, as published in 2003,
recommends using 3 percent and 7 percent discount rates as "default" values, Circular A-4 also
reminds agencies that "different regulations may call for different emphases in the analysis,
depending on the nature and complexity of the regulatory issues and the sensitivity of the benefit
and cost estimates to the key assumptions." On discounting, Circular A-4 recognizes that
"special ethical considerations arise when comparing benefits and costs across generations," and
Circular A-4 acknowledges that analyses may appropriately "discount future costs and
consumption benefits.. .at a lower rate than for intragenerational analysis." In the 2015 Response
to Comments on the Social Cost of Carbon for Regulatory Impact Analysis, OMB, EPA, and the
other IWG members recognized that "Circular A-4 is a living document" and "the use of 7
percent is not considered appropriate for intergenerational discounting. There is wide support for
this view in the academic literature, and it is recognized in Circular A-4 itself." Thus, the EPA
concludes that a 7 percent discount rate is not appropriate to apply to value the SC-GHGs in the
analysis presented in this RIA. In this analysis, to calculate the present and annualized values of
climate benefits, the EPA uses the same discount rate as the rate used to discount the value of
damages from future GHG emissions, for internal consistency. That approach to discounting
follows the same approach that the February 2021 SC-GHG TSD recommends "to ensure
internal consistency—i.e., future damages from climate change using the SC-GHG at 2.5 percent
should be discounted to the base year of the analysis using the same 2.5 percent rate." EPA has
also consulted the National Academies' 2017 recommendations on how SC-GHG estimates can
"be combined in RIAs with other cost and benefits estimates that may use different discount
46 GHG emissions are stock pollutants, where damages are associated with what has accumulated in the atmosphere
over time, and they are long lived such that subsequent damages resulting from emissions today occur over many
decades or centuries depending on the specific GHG under consideration. In calculating the SC-GHG, the stream of
future damages to agriculture, human health, and other market and non-market sectors from an additional unit of
emissions are estimated in terms of reduced consumption (or consumption equivalents). Then that stream of future
damages is discounted to its present value in the year when the additional unit of emissions was released. Given the
long time horizon over which the damages are expected to occur, the discount rate has a large influence on the
present value of future damages.
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rates." The National Academies reviewed "several options," including "presenting all discount
rate combinations of other costs and benefits with [SC-GHG] estimates."
While the IWG works to assess how best to incorporate the latest, peer reviewed science
to develop an updated set of SC-GHG estimates, it recommended the interim estimates to be the
most recent estimates developed by the IWG prior to the group being disbanded in 2017. The
estimates rely on the same models and harmonized inputs and are calculated using a range of
discount rates. As explained in the February 2021 SC-GHG TSD, the IWG has concluded that it
is appropriate for agencies to revert to the same set of four values drawn from the SC-GHG
distributions based on three discount rates as were used in regulatory analyses between 2010 and
2016 and subject to public comment. For each discount rate, the IWG combined the distributions
across models and socioeconomic emissions scenarios (applying equal weight to each) and then
selected a set of four values for use in agency analyses: an average value resulting from the
model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a fourth
value, selected as the 95th percentile of estimates based on a 3 percent discount rate. The fourth
value was included to provide information on potentially higher-than-expected economic impacts
from climate change, conditional on the 3 percent estimate of the discount rate. As explained in
the February 2021 SC-GHG TSD, this update reflects the immediate need to have an operational
SC-GHG that was developed using a transparent process, peer-reviewed methodologies, and the
science available at the time of that process. Those estimates were subject to public comment in
the context of dozens of proposed rulemakings as well as in a dedicated public comment period
in 2013.
Table 4-11 summarizes the interim SC-CO2 estimates for the years 2025 to 2040. These
estimates are reported in 2019 dollars but are otherwise identical to those presented in the IWG's
2016 SC-GHG TSD (IWG, 2016b). For purposes of capturing uncertainty around the SC-CO2
estimates in analyses, the 2021 SC-GHG TSD emphasizes the importance of considering all four
of the SC-CO2 values. The SC-CO2 increases over time within the models - i.e., the societal
harm from one metric ton emitted in 2030 is higher than the harm caused by one metric ton
emitted in 2025 - because future emissions produce larger incremental damages as physical and
economic systems become more stressed in response to greater climatic change, and because
GDP is growing over time and many damage categories are modeled as proportional to GDP.
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Table 4-11 Interim Social Cost of Carbon Values, 2025-2040 (2019 dollars per Metric
Tonne CO2)
Discount Rate and Statistic
5%
3%
2.50%
3%
Emissions Year
Average
Average
Average
95th Percentile
2025
$17
$56
$82
$167
2026
$17
$57
$83
$171
2027
$18
$58
$85
$174
2028
$18
$59
$86
$178
2029
$19
$60
$87
$181
2030
$19
$61
$88
$184
2031
$20
$62
$90
$188
2032
$20
$63
$91
$192
2033
$21
$64
$92
$196
2034
$21
$66
$94
$200
2035
$22
$67
$95
$203
2036
$23
$68
$96
$207
2037
$23
$69
$98
$211
2038
$24
$70
$99
$215
2039
$24
$71
$101
$218
2040
$25
$72
$102
$222
Note: These SC-CO2 values are identical to those reported in the 2016 SC-GHG TSD (IWG 2016a) adjusted for
inflation to 2019 dollars using the annual GDP Implicit Price Deflator values in the U. S. Bureau of Economic
Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA 2021). The values are stated in $/metric tonne CO2 (1 metric tonne
equals 1.102 short tons) and vary depending on the year of CO2 emissions. This table displays the values rounded to
the nearest dollar; the annual unrounded values used in the calculations in this RIA are available on OMB's website:
https://www.whitehouse.gOv/omb/information-regulatory-affairs/regulatory-matters/#scghgs.
Source: Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under
E.O. 13990 (IWG 2021)
There are a number of limitations and uncertainties associated with the SC-CO2 estimates
presented in Table 4-11. Some uncertainties are captured within the analysis, while other areas of
uncertainty have not yet been quantified in a way that can be modeled. Figure 4-2 presents the
quantified sources of uncertainty in the form of frequency distributions for the SC-CO2 estimates
for emissions in 2030. The distributions of SC-CO2 estimates reflect uncertainty in key model
parameters such as the equilibrium climate sensitivity, as well as uncertainty in other parameters
set by the original model developers. To highlight the difference between the impact of the
discount rate and other quantified sources of uncertainty, the bars below the frequency
distributions provide a symmetric representation of quantified variability in the SC-CO2
estimates for each discount rate. As illustrated by the figure, the assumed discount rate plays a
critical role in the ultimate estimate of the SC-CO2. This is because CO2 emissions today
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continue to impact society far out into the future, so with a higher discount rate, costs that accrue
to future generations are weighted less, resulting in a lower estimate. As discussed in the 2021
SC-GHG TSD, there are other sources of uncertainty that have not yet been quantified and are
thus not reflected in these estimates.
Figure 4-2 Frequency Distribution of SC-CO2 Estimates for 2030
E
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o
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5% Average = $18
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3% Average = $57
2.5% Average = $83
3%
95th Pet. = $174
i
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0 20
40
I I I I I I I
60 80
100
I I I I I I I I I I I I I I I I I I I I I I I I I I I I
120 140 160 180 200 220 240
of Simulations
I I I I I I I I I I I I
260 280 300
Social Cost of Carbon in 2030 [2016$ / metric ton C02]
The interim SC-CO2 estimates presented in Table 4-11 have a number of limitations.
First, the current scientific and economic understanding of discounting approaches suggests
discount rates appropriate for intergenerational analysis in the context of climate change are
likely to be less than 3 percent, near 2 percent or lower (IWG, 2021). Second, the IAMs used to
produce these interim estimates do not include all of the important physical, ecological, and
economic impacts of climate change recognized in the climate change literature and the science
underlying their "damage functions" - i.e., the core parts of the IAMs that map global mean
temperature changes and other physical impacts of climate change into economic (both market
and nonmarket) damages - lags behind the most recent research. For example, limitations
include the incomplete treatment of catastrophic and non-catastrophic impacts in the integrated
assessment models, their incomplete treatment of adaptation and technological change, the
incomplete way in which inter-regional and intersectoral linkages are modeled, uncertainty in the
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extrapolation of damages to high temperatures, and inadequate representation of the relationship
between the discount rate and uncertainty in economic growth over long time horizons.
Likewise, the socioeconomic and emissions scenarios used as inputs to the models do not reflect
new information from the last decade of scenario generation or the full range of projections.
The modeling limitations do not all work in the same direction in terms of their influence
on the SC-CO2 estimates. However, as discussed in the February 2021 SC-GHG TSD, the IWG
has recommended that, taken together, the limitations suggest that the SC-CO2 estimates used in
this RIA likely underestimate the damages from CO2 emissions. EPA concurs that the values
used in this RIA conservatively underestimate the rule's climate benefits. In particular, the
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007),
which was the most current IPCC assessment available at the time when the IWG decision over
the ECS input was made, concluded that SC-CO2 estimates "very likely.. .underestimate the
damage costs" due to omitted impacts. Since then, the peer-reviewed literature has continued to
support this conclusion, as noted in the IPCC's Fifth Assessment report and other recent
scientific assessments (IPCC, 2014, 2018, 2019a, 2019b; National Academies of Sciences and
Medicine, 2016; USGCRP, 2016, 2018)
These assessments confirm and strengthen the science, updating projections of future
climate change and documenting and attributing ongoing changes. For example, sea level rise
projections from the IPCC's Fourth Assessment report ranged from 18 to 59 centimeters by the
2090s relative to 1980-1999, while excluding any dynamic changes in ice sheets due to the
limited understanding of those processes at the time (IPCC 2007). A decade later, the Fourth
National Climate Assessment projected a substantially larger sea level rise of 30 to 130
centimeters by the end of the century relative to 2000, while not ruling out even more extreme
outcomes (USGCRP, 2018). EPA has reviewed and considered the limitations of the models
used to estimate the interim SC-GHG estimates and concurs with the February 2021 SC-GHG
TSD's assessment that, taken together, the limitations suggest that the interim SC-GHG
estimates likely underestimate the damages from GHG emissions.
The February 2021 SC-GHG TSD briefly previews some of the recent advances in the
scientific and economic literature that the IWG is actively following and that could provide
guidance on, or methodologies for, addressing some of the limitations with the interim SC-GHG
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estimates. The IWG is currently working on a comprehensive update of the SC-GHG estimates
taking into consideration recommendations from the National Academies of Sciences,
Engineering and Medicine, recent scientific literature, public comments received on the February
2021 SC-GHG TSD and other input from experts and diverse stakeholder groups (National
Academies 2017). While that process continues, the EPA is continuously reviewing
developments in the scientific literature on the SC-GHG, including more robust methodologies
for estimating damages from emissions, and looking for opportunities to further improve SC-
GHG estimation going forward. Most recently, the EPA presented a draft set of updated SC-
GHG estimates within a sensitivity analysis in the regulatory impact analysis of the EPA's
November 2022 supplemental proposal for oil and gas standards that that aims to incorporate
recent advances in the climate science and economics literature (U.S. EPA, 2022b, 2022e).
Specifically, the draft updated methodology incorporates new literature and research consistent
with the National Academies near-term recommendations on socioeconomic and emissions
inputs, climate modeling components, discounting approaches, and treatment of uncertainty, and
an enhanced representation of how physical impacts of climate change translate to economic
damages in the modeling framework based on the best and readily adaptable damage functions
available in the peer reviewed literature. The EPA solicited public comment on the sensitivity
analysis and the accompanying draft technical report, which explains the methodology
underlying the new set of estimates, in the docket for the proposed Oil and Gas rule. The EPA is
also embarking on an external peer review of this technical report. More information about this
process and public comment opportunities is available on EPA's website.47 EPA's draft technical
report will be among the many technical inputs available to the IWG as it continues its work.
47 See https://www.epa.gov/environmental-economics/scghg
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Table 4-12 shows the estimated monetized value of the estimated changes in CO2
emissions the proposed option and the more-stringent alternative. EPA estimated the dollar value
of the C02-related effects for each analysis year between 2028 and 2037 by applying the SC-CO2
estimates, shown in Table 4-12, to the estimated changes in CO2 emissions in the corresponding
year under the regulatory options. Note the less stringent regulatory alternative has no quantified
emissions reductions associated with the proposed requirements for PM CEMS and the removal
of startup definition number two. As a result, there are no quantified benefits associated with this
regulatory option.
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Table 4-12 Estimated Climate Benefits from Changes in CO2 Emissions for 2028, 2030,
and 2035 (millions of 2019 dollars)3
5%
3%
2.5%
3%
Regulatory
Alternative
Year
Average
Average
Average
95th
Percentile
2028
$4
$13
$19
$40
Proposed
Option
2030
$16
$50
$72
$150
2035
$102
$308
$439
$939
2028
$398
$1,292
$1,882
$3,893
More-Stringent
Alternative
2030
$166
$528
$765
$1,597
2035
$64
$193
$275
$588
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate). We emphasize the importance and value of considering the benefits calculated using all four
SC-CO2 estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous
Oxide Interim Estimates under E.O. 13990 (IWG 2021), a consideration of climate benefits calculated using
discount rates below 3 percent, including 2 percent and lower, are also warranted when discounting intergenerational
impacts.
Table 4-13 Stream of Projected Climate Benefits under Proposed Rule from 2028
through 2037 (millions of 2019 dollars)
SC-CO2 Discount Rate and Statistic
5%
3%
2.50%
3%
Emissions Year
Average
Average
Average
95th Percentile
2028*
$4
$13
$19
$40
2029
$15
$49
$71
$150
2030*
$16
$50
$72
$150
203 1
$16
$51
$73
$150
2032
$94
$290
$420
$890
2033
$96
$300
$430
$900
2034
$99
$300
$430
$920
2035*
$100
$310
$440
$940
2036
$100
$310
$450
$960
2037
$110
$320
$450
$970
3% Discount Rate for PV and EAV Calculations
Present Value
$470
$1,400
$2,100
$4,400
Equivalent
Annualized Value
$55
$170
$240
$510
* IPM analysis years.
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Table 4-14 Stream of Projected Climate Benefits under More Stringent Regulatory
Option from 2028 through 2037 (millions of 2019 dollars)
SC-CO2 Discount Rate and Statistic
5%
3%
2.50%
3%
Emissions Year
Average
Average
Average
95th Percentile
2028*
$400
$1,300
$1,900
$3,900
2029
$160
$520
$750
$1,600
2030*
$170
$530
$770
$1,600
2031
$170
$540
$780
$1,600
2032
$59
$180
$260
$560
2033
$60
$190
$270
$570
2034
$62
$190
$270
$580
2035*
$64
$190
$280
$590
2036
$65
$200
$280
$600
2037
$67
$200
$280
$610
3% Discount Rate for PV and EAV Calculations
Present Value
$1,000
$3,200
$4,700
$9,700
Equivalent
Annualized Value
$120
$380
$550
$1,100
* IPM analysis years.
4.5 Water Quality and Availability Benefits
As described in Section 3, this rule is expected to lead to shifts in electricity production
away from fossil-fired steam generation towards renewable and natural gas generation. There are
several negative health, ecological, and productivity effects associated with water effluent and
intake from coal generation that will be avoided, and the benefits are qualitatively described
below. For additional discussion of these effects and their consequent effect on welfare, see the
Benefit and Cost Analysis for Revisions to the Effluent Limitations Guidelines and Standards for
the Steam Electric Power Generating Point Source Category (U.S. EPA 2020b).
4.5.1.1 Potential Water Quality Benefits of Reducing Coal-Fired Power Generation
Discharges of wastewater from coal-fired power plants can contain toxic and
bioaccumulative pollutants (e.g., selenium, mercury, arsenic, nickel), halogen compounds
(containing bromide, chloride, or iodide), nutrients, and total dissolved solids (TDS), which can
cause human health and environmental harm through surface water and fish tissue
contamination. Pollutants in coal combustion wastewater are of particular concern because they
can occur in large quantities (i.e., total pounds) and at high concentrations in discharges and
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leachate to groundwater and surface waters. These potential beneficial effects follow directly
from reductions in pollutant loadings to receiving waters, and indirectly from other changes in
plant operations. The potential benefits come in the form of reduced morbidity, mortality, and on
environmental quality and economic activities; reduction in water use, which provides benefits in
the form of increased availability of surface water and groundwater; and reductions in the use of
surface impoundments to manage Coal Combustion Residual wastes, with benefits in the form of
avoided cleanup and other costs associated with impoundment releases.
Reducing coal-fired power generation affects human health risk by changing exposure to
pollutants in water via two principal exposure pathways: (1) treated water sourced from surface
waters affected by coal-fired power plant discharges and (2) fish and shellfish taken from
waterways affected by coal-fired power plant discharges. The human health benefits from
surface water quality improvements may include drinking water benefits, fish consumption
benefits, and other complimentary measures.
In addition, reducing coal-fired power generation can affect the ecological condition and
recreation use effects from surface water quality changes. EPA expects the ecological impacts
from reducing coal-fired power plant discharges could include habitat changes for fresh- and
saltwater plants, invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that
prey on aquatic organisms exposed to pollutants from coal combustion. The change in pollutant
loadings has the potential to result in changes in ecosystem productivity in waterways and the
health of resident species, including threatened and endangered (T&E) species. Loadings from
coal-fired power generation have the potential to impact the general health of fish and
invertebrate populations, their propagation to waters, and fisheries for both commercial and
recreational purposes. Changes in water quality also have the potential to impact recreational
activities such as swimming, boating, fishing, and water skiing.
Potential economic productivity effects may stem from changes in the quality of public
drinking water supplies and irrigation water; changes in sediment deposition in reservoirs and
navigational waterways; and changes in tourism, commercial fish harvests, and property values.
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4.5.1.2 Drinking Water
Pollutants discharged by coal-fired power plants to surface waters may affect the quality
of water used for public drinking supplies. In turn these impacts to public water supplies have the
potential to affect the costs of drinking water treatment (e.g., filtration and chemical treatment)
by changing eutrophication levels and pollutant concentrations in source waters. Eutrophication
is one of the main causes of taste and odor impairment in drinking water, which has a major
negative impact on public perceptions of drinking water safety. Additional treatment to address
foul tastes and odors to bring the finished water into compliance with EPA's National Secondary
Drinking Water Treatment Standards can significantly increase the cost of public water supply.
Likewise, public drinking water supplies are subject to National Primary Drinking Water
Standards that have set legally enforceable maximum contaminant levels (MCLs), for a number
of pollutants, like metals, discharged from coal-fired power plants. Drinking water systems
downstream from these power plants may be required to treat source water to remove the
contaminants to levels below the MCL in the finished water. This treatment will also increase
costs at drinking water treatment plants. Episodic releases from coal fired power plants may be
detected only after the completion of a several month round of compliance monitoring at
drinking water treatment plants, and there could also be a lag between detection of changes in
source water contaminants and the system implementing treatment to address the issue. This lag
may result in consumers being exposed to these contaminants through ingestion, inhalation, and
skin absorption. The constituents found in the power plant discharge may also interact with
drinking water treatment processes and contribute to the formation of disinfection byproducts
that can have adverse human health impacts.
4.5.1.3 Fish Consumption
Recreational and subsistence fishers (and their household members) who consume fish
caught in the reaches downstream of coal-fired power plants may value changes in pollutant
concentrations in fish tissue. See U.S. EPA (2020b) for a demonstration of the changes in risk to
human health from exposure to contaminated fish tissue. This document describes the
neurological effects to children ages 0 to 7 from exposure to lead; the neurological effects to
infants from in-utero exposure to mercury; the incidence of skin cancer from exposure to arsenic;
and the reduced risk of other cancer and non-cancer toxic effects.
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4.5.1.4 Changes in Surface Water Quality
Reducing coal-fired power plant discharges may affect the value of ecosystem services
provided by surface waters through changes in the habitats or ecosystems (aquatic and
terrestrial). Society values changes in ecosystem services by a number of mechanisms, including
increased frequency of use and improved quality of the habitat for recreational activities (e.g.,
fishing, swimming, and boating). Individuals also value the protection of habitats and species
that may reside in waters that receive water discharges from coal-fired power plants, even when
those individuals do not use or anticipate future use of such waters for recreational or other
purposes, resulting in nonuse values.
4.5.1.5 Impacts on Threatened and Endangered Species
For T&E species, even minor changes to reproductive rates and mortality levels may
represent a substantial portion of annual population variation. Therefore, changing the discharge
of coal-fired power plant pollutants to aquatic habitats has the potential to impact the
survivability of some T&E species living in these habitats. The economic value for these T&E
species primarily comes from the nonuse values people hold for the survivorship of both
individual organisms and species survival.
4.5.1.6 Changes in Sediment Contamination
Water effluent discharges from coal-fired power plants can also contaminate waterbody
sediments. For example, sediment adsorption of arsenic, selenium, and other pollutants found in
water discharges can result in accumulation of contaminated sediment on stream and lake beds,
posing a particular threat to benthic (i.e., bottom-dwelling) organisms. These pollutants can later
be re-released into the water column and enter organisms at different trophic levels.
Concentrations of selenium and other pollutants in fish tissue of organisms of lower trophic
levels can bio-magnify through higher trophic levels, posing a threat to the food chain at large
(Ruhletal., 2012)
4.5.1.7 Reservoir Capacity and Sedimentation Changes in Navigational Waterways
Reservoirs serve many functions, including storage of drinking and irrigation water
supplies, flood control, hydropower supply, and recreation. Streams can carry sediment into
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reservoirs, where it can settle and cause buildup of sediment layers over time, reducing reservoir
capacity (Graf et al., 2010, 2011) and the useful life of reservoirs unless measures such as
dredging are taken to reclaim capacity (Hargrove et al., 2010; Miranda, 2017). Likewise,
navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are prone to
reduced functionality due to sediment build-up, which can reduce the navigable depth and width
of the waterway (Clark et al., 1985; Ribaudo and Johansson, 2006). For many navigable waters,
periodic dredging is necessary to remove sediment and keep them passable. Dredging of
reservoirs and navigable waterways can be costly. EPA expects that changes in suspended solids
effluent discharge from coal-fired power plants could reduce sediment loadings to surface waters
decreasing reservoir and navigable waterway maintenance costs by changing the frequency or
volume of dredging activity.
4.5.1.8 Changes in Water Withdrawals
A reduction in water consumption from coal-fired power plants may benefit aquatic and
riparian species downstream of the power plant intake through the provision of additional water
resources in the face of drying conditions and increased rainfall variability. In a study completed,
in 2011, by the U.S. Department of Energy's National Renewable Energy Laboratory (2011),
water consumption, which is defined as water removed from the immediate water environment
and can include cooling water evaporation, cleaning, and process related water use including flue
gas desulfurization, was found to range from 100- 1,100 gal/MWh at generic coal-fired power
plants. This study also found that water withdraws, defined as the amount of water removed from
the ground or diverted from a water source for use, ranged from 300 - 50,000 gal/MWh at a
generic coal-fired power plant. Reductions in water consumption and withdraws will lower the
number of aquatic organisms impinged and entrained by the power plant's water filtration and
cooling systems.
4.6 Total Benefits
Table 4-15 through Table 4-17 present the total health and climate benefits for the
proposed rule and the more stringent alternative.
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Table 4-15 Combined PM2.5 and 03-related Health Benefits and Climate Benefits for the
Proposed Requirements and More Stringent Alternative for 2028 (millions of 2019 dollars)
PM2.5 and 03-related Health Benefits
and Climate Benefits
(Discount Rate Applied to Health Benefits)
SC-CO2 Discount Rate
and Statistic
3%
7%
Climate Benefits Only"
Proposed Rule
5% (average)
150
130
4.1
3% (average)
160
140
13
2.5% (average)
160
150
19
3% (95th percentile)
180
170
40
More Stringent Alternative
5% (average)
3,500
3,200
400
3% (average)
4,400
4,100
1,300
2.5% (average)
5,000
4,700
1,900
3% (95th percentile)
7,000
6,700
3,900
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
Table 4-16 Combined PM2.5 and 03-related Health Benefits and Climate Benefits for the
Proposed Requirements and More Stringent Alternative for 2030 (millions of 2019 dollars)
PM2.5 and 03-related Health Benefits
and Climate Benefits
(Discount Rate Applied to Health Benefits)
SC-CO2 Discount Rate
and Statistic
3%
7%
Climate Benefits Only"
Proposed Rule
5% (average)
170
150
16
3% (average)
200
190
50
2.5% (average)
220
210
72
3% (95th percentile)
300
290
150
More Stringent Alternative
5% (average)
1,000
940
170
3% (average)
1,400
1,300
530
2.5% (average)
1,600
1,500
770
3% (95th percentile)
2,500
2,400
1,600
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
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Table 4-17 Combined PM2.5 and 03-related Health Benefits and Climate Benefits for the
Proposed Requirements and More Stringent Alternative for 2035 (millions of 2019 dollars)
PM2.5 and 03-related Health Benefits
and Climate Benefits
SC-CO2 Discount Rate
(Discount Rate Applied to Health Benefits)
and Statistic
3%
7%
Climate Benefits Only3
Proposed Rule
5% (average)
430
400
100
3% (average)
640
610
310
2.5% (average)
770
740
440
3% (95th percentile)
1,300
1,200
940
More Stringent Alternative
5% (average)
1,600
1,400
64
3% (average)
1,700
1,600
190
2.5% (average)
1,800
1,600
280
3% (95th percentile)
2,100
1,900
590
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
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Table 4-18 Stream of Combined PM2.5 and Ch-related Health Benefits and Climate
Benefits for the Proposed Rule from 2028 through 2037 (millions of 2019 dollars)3
Values Calculated using 3% Discount Rate
PM2.5 and
PM2.5 and
Climate
Year
03-related
03-related
Benefits
Health
Climate
Total
Health
(discounted
Total
Benefits
Benefits"
Benefits
Benefits
at 3%)
Benefits
2028
140
13
160
130
13
140
2029
150
49
200
130
49
180
2030
150
50
200
140
50
190
2031
190
51
240
170
51
220
2032
220
290
520
200
290
490
2033
260
300
560
230
300
530
2034
300
300
600
270
300
570
2035
330
310
640
300
310
610
2036
370
310
680
330
310
650
2037
410
320
720
360
320
680
Present Value
1,900
1,400
3,300
1,100
1,400
2,600
Equivalent
Annualized
220
170
390
160
170
330
Values Calculated using 7% Discount Rate
Value
a Climate benefits are based on reductions in CO2 emissions and are calculated using four different estimates of the
social cost of carbon dioxide (SC-CO2): model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th
percentile at 3 percent discount rate. The 95th percentile estimate is included to provide information on potentially
higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount
rate. For the presentational purposes of this table, we show the climate benefits associated with the average SC-CO2
at a 3 percent discount rate, but the Agency does not have a single central SC-CO2 point estimate. Climate benefits
in this table are discounted using a 3 percent discount rate to obtain the PV and EAV estimates in the table. We
emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. Section
4.4 of the RIA presents estimates of the projected climate benefits of this proposal using all four rates. We note that
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is
warranted when discounting intergenerational impacts.
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Table 4-19 Stream of Combined PM2.5 and Ch-related Health Benefits and Climate
Benefits for the More Stringent Regulatory Option from 2028 through 2037 (millions of
2019 dollars)3
Values Calculated using 3% Discount Rate
PM2.5 and
PM2.5 and
Climate
03-related
03-related
Benefits
Health
Climate
Total
Health
(discounted
Total
Year
Benefits
Benefits
Benefits
Benefits
at 3%)
Benefits
2028
3,100
1,300
4,400
2,800
1,300
4,100
2029
2,000
520
2,500
1,800
520
2,300
2030
860
530
1,400
770
530
1,300
2031
990
540
1,500
890
540
1,400
2032
1,100
180
1,300
1,000
180
1,200
2033
1,300
190
1,400
1,100
190
1,300
2034
1,400
190
1,600
1,200
190
1,400
2035
1,500
190
1,700
1,400
190
1,600
2036
1,600
200
1,800
1,500
200
1,700
2037
1,800
200
2,000
1,600
200
1,800
Present Value
12,000
3,200
15,000
7,700
3,200
11,000
Equivalent
Annualized
1,400
380
1,800
1,100
380
1,500
Values Calculated using 7% Discount Rate
Value
a Climate benefits are based on reductions in CO2 emissions and are calculated using four different estimates of the
social cost of carbon dioxide (SC-CO2): model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th
percentile at 3 percent discount rate. The 95th percentile estimate is included to provide information on potentially
higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount
rate. For the presentational purposes of this table, we show the climate benefits associated with the average SC-CO2
at a 3 percent discount rate, but the Agency does not have a single central SC-CO2 point estimate. Climate benefits
in this table are discounted using a 3 percent discount rate to obtain the PV and EAV estimates in the table. We
emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. Section
4.4 of the RIA presents estimates of the projected climate benefits of this proposal using all four rates. We note that
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is
warranted when discounting intergenerational impacts.
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4.7 References
Agency for Toxic Substances and Disease Registry. (2022). Toxicological Profile for Mercury
(Draftfor Public Comment). (CAS#: 7439-97-6). U.S. Center for Desease Control.
Available at: https://wwwn.cdc.gov/TSP/ToxProfiles/ToxProfiles.aspx?id=l 15&tid=24
Anthoff, D., & Tol, R. S. J. (2013a). Erratum to: The uncertainty about the social cost of carbon:
A decomposition analysis using FUND (vol 117, pg 515, 2013). Climatic Change,
727(2), 413-413. doi: 10.1007/sl0584-013-0959-l
Anthoff, D., & Tol, R. S. J. (2013b). The uncertainty about the social cost of carbon: A
decomposition analysis using FUND. Climatic Change, 117(3), 515-530.
doi: 10.1007/s 10584-013 -0706-7
Bell, M. L., Dominici, F., & Samet, J. M. (2005). A meta-analysis of time-series studies of ozone
and mortality with comparison to the national morbidity, mortality, and air pollution
study .Epidemiology, 16(4), 436-445. doi:10.1097/01.ede.0000165817.40152.85
Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., & Dominici, F. (2004). Ozone and short-
term mortality in 95 US urban communities, 1987-2000. JAMA, 292(19), 2372-2378.
doi: 10.1001/jama.292.19.2372
Clark, E. H., Haverkamp, J. A., & Chapman, W. (1985). Eroding soils. The off-farm impacts.
Washington, DC: Conservation Foundation.
Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., . . . Schwartz, J. D. (2017).
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5 ECONOMIC IMPACTS
5.1 Overview
Economic impact analyses focus on changes in market prices and output levels. If
changes in market prices and output levels in the primary markets are significant enough,
impacts on other markets may also be examined. Both the magnitude of costs needed to comply
with a rule and the distribution of these costs among affected facilities can have a role in
determining how the market will change in response to a rule. This section analyzes the potential
impacts on small entities and the potential labor impacts associated with this rulemaking. For
additional discussion of impacts on fuel use and electricity prices, see Section 3.
5.2 Small Entity Analysis
For the proposed rule, EPA performed a small entity screening analysis for impacts on all
affected EGUs and non-EGU facilities by comparing compliance costs to historic revenues at the
ultimate parent company level. This is known as the cost-to-revenue or cost-to-sales test, or the
"sales test." The sales test is an impact methodology EPA employs in analyzing entity impacts as
opposed to a "profits test," in which annualized compliance costs are calculated as a share of
profits. The sales test is frequently used because revenues or sales data are commonly available
for entities impacted by EPA regulations, and profits data normally made available are often not
the true profit earned by firms because of accounting and tax considerations. Also, the use of a
sales test for estimating small business impacts for a rulemaking is consistent with guidance
offered by EPA on compliance with the Regulatory Flexibility Act (REA)48 and is consistent with
guidance published by the U.S. Small Business Administration's (SB A) Office of Advocacy that
suggests that cost as a percentage of total revenues is a metric for evaluating cost increases on
small entities in relation to increases on large entities.49
48 See U.S. EPA. (2006). Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as Amended by the Small
Business and Regulatory Enforcement Fairness Act. Available at: https://www.epa.gov/sites/production/files/2015-
06/documents/guidance-regflexact.pdf
49 See U.S. SBA Office of Advocacy. (2017). A Guide For Government Agencies: How To Comply With The
Regulatory Flexibility Act. Available at: https://advocacy.sba.gov/2017/08/31/a-guide-for-government-agencies-
how-to-comply-with-the-regulatory-flexibility-act
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5.2.1 Methodology
This section presents the methodology and results for estimating the impact of the rule on
small EGU entities in the year of compliance, 2028, based on the following endpoints:
• annual economic impacts of the proposal on small entities, and
• ratio of small entity impacts to revenues from electricity generation.
In this analysis, we chose to examine the projected impacts of the more stringent
regulatory option on small entities in order to present a scenario of "maximum cost impact". As
we explain in the Section 5.2.3, we conclude that the projected impacts of the more stringent
regulatory alternative do not constitute a Significant Impact on a Substantial Number of Small
Entities (SISNOSE). As projected cost impacts of the proposed rule less stringent options are
dominated by cost impacts of the more stringent alternative, a no SISNOSE conclusion for the
more stringent option can be extended to the proposed rule and less stringent option.
For this analysis, EPA first considered EGUs that are subject to MATS requirements and
for which EPA assumed additional controls would be necessary to meet the requirements
constituted by the more stringent regulatory option. We then refined this list of MATS-affected
EGUs, complementing the list with units for which the projected impact of the more stringent
option exceeds either of the two criteria below relative to the baseline:
• Fuel use (BTUs) changes by +/- 1 percent or more
• Generation (GWh) changes by +/- 1 percent or more
Please see Section 3 for more discussion of the power sector modeling.
Based on these criteria, EPA identified a total of 358 potentially affected EGUs
warranting examination in 2028 in this RFA analysis. Next, we determined power plant
ownership information, including the name of associated owning entities, ownership shares, and
each entity's type of ownership. We primarily used data from Hitachi - Power Grids, The
Velocity Suite (c) 2020 ("VS"), supplemented by limited research using publicly available data.
Majority owners of power plants with affected EGUs were categorized as one of the seven
ownership types. These ownership types are:
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1. Investor-Owned Utility (IOU): Investor-owned assets (e.g., a marketer, independent
power producer, financial entity) and electric companies owned by stockholders, etc.
2. Cooperative (Co-Op): Non-profit, customer-owned electric companies that generate
and/or distribute electric power.
3. Municipal: A municipal utility, responsible for power supply and distribution in a small
region, such as a city.
4. Sub-division: Political subdivision utility is a county, municipality, school district,
hospital district, or any other political subdivision that is not classified as a municipality
under state law.
5. Private: Similar to an investor-owned utility, however, ownership shares are not openly
traded on the stock markets.
6. State: Utility owned by the state.
7. Federal: Utility owned by the federal government.
Next, EPA used both the D&B Hoovers online database and the VS database to identify
the ultimate owners of power plant owners identified in the VS database. This was necessary, as
many majority owners of power plants (listed in VS) are themselves owned by other ultimate
parent entities (listed in D&B Hoovers). In these cases, the ultimate parent entity was identified
via D&B Hoovers, whether domestically or internationally owned.
EPA followed SBA size standards to determine which non-government ultimate parent
entities should be considered small entities in this analysis. These SBA size standards are
specific to each industry, each having a threshold level of either employees, revenue, or assets
below which an entity is considered small. SBA guidelines list all industries, along with their
associated North American Industry Classification System (NAICS) code and SBA size
standard. Therefore, it was necessary to identify the specific NAICS code associated with each
ultimate parent entity in order to understand the appropriate size standard to apply. Data from
D&B Hoovers was used to identify the NAICS codes for most of the ultimate parent entities. In
many cases, an entity that is a majority owner of a power plant is itself owned by an ultimate
parent entity with a primary business other than electric power generation. Therefore, it was
necessary to consider SBA entity size guidelines for the range of NAICS codes listed in Table
5-1. This table represents the range of NAICS codes and areas of primary business of ultimate
parent entities that are majority owners of potentially affected EGUs in EPA's IPM base case.
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Table 5-1
SBA Size Standards by NAICS Code
Size Standard Size Standard
(millions of (number of
NAICS Code
NAICS U.S. Industry Title
dollars)
employees)
211120
Crude Petroleum Extraction
1,250
212221
Gold Ore Mining
1,500
221111
Hydroelectric Power Generation
500
221112
Fossil Fuel Electric Power Generation
750
221113
Nuclear Electric Power Generation
750
221114
Solar Electric Power Generation
250
221115
Wind Electric Power Generation
250
221116
Geo thermal Electric Power Generation
250
221117
Biomass Electric Power Generation
250
221118
Other Electric Power Generation
250
221121
Electric Bulk Power Transmission and Control
500
221122
Electric Power Distribution
1,000
221210
Natural Gas Distribution
1,000
221310
Water Supply and Irrigation Systems
$41.00
221320
Sewage Treatment Facilities
$35.00
221330
Steam and Air Conditioning Supply
$30.00
311221
Wet Corn Milling
1,250
311224
Soybean and Other Oilseed Processing
1,000
322121
Paper (except Newsprint) Mills
1,250
325611
Soap and Other Detergent Manufacturing
1,000
325920
Explosives Manufacturing
750
331110
Iron and Steel Mills and Ferroalloy Manufacturing
1,500
332313
Plate Work Manufacturing
750
332911
Industrial Valve Manufacturing
750
333611
Turbine and Turbine Generator Set Unit Manufacturing
1,500
333613
Mechanical Power Transmission Equipment Manufacturing
750
423520
Coal and Other Mineral and Ore Merchant Wholesalers
200
423990
Other Miscellaneous Durable Goods Merchant Wholesalers
100
424690
Other Chemical and Allied Products Merchant Wholesalers
175
424720
Petroleum and Petroleum Products Merchant Wholesalers
200
522110
Commercial Banking
$750.00
523210
Securities and Commodity Exchanges
$47.00
523910
Miscellaneous Intermediation
$44.25
523930
Investment Advice
$41.50
524126
Direct Property and Casualty Insurance Carriers
1,500
525910
Open-End Investment Funds
$37.50
525990
Other Financial Vehicles
$40.00
541330
Engineering Services
$22.50
541611
Administrative Management and General Management
$21.50
Consulting Services
541715
Research and Development in the Physical, Engineering, and Life Sciences
1,000
(except Nanotechnology and Biotechnology)
551112
Offices of Other Holding Companies
$45.50
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NAICS Code
NAICS U.S. Industry Title
Size Standard
(millions of
dollars)
Size Standard
(number of
employees)
611310
Colleges, Universities and Professional Schools
$30.50
721110
Hotels (except Casino Hotels) and Motels
$35.00
813910
Business Associations
$13.50
Note: Based on size standards effective at the time EPA conducted this analysis (SBA size standards, effective
December 19, 2022. Available at the following link: https://www.sba.gov/document/support--table-size-standards).
Source: SBA, 2022.
EPA compared the relevant entity size criterion for each ultimate parent entity to the SBA
size standard noted in Table 5-1. We used the following data sources and methodology to
estimate the relevant size criterion values for each ultimate parent entity:
• Employment, Revenue, and Assets: EPA used the D&B Hoovers database as the
primary source for information on ultimate parent entity employee numbers, revenue, and
assets.50 In parallel, EPA also considered estimated revenues from affected EGUs based
on analysis of IPM parsed-ftle51 estimates for the baseline run for 2028. EPA assumed
that the ultimate parent entity revenue was the larger of the two revenue estimates. In
limited instances, supplemental research was also conducted to estimate an ultimate
parent entity's number of employees, revenue, or assets.
• Population: Municipal entities are defined as small if they serve populations of less than
50,000.52 EPA primarily relied on data from the Ventyx database and the U.S. Census
Bureau to inform this determination.
Ultimate parent entities for which the relevant measure is less than the SBA size standard were
identified as small entities and carried forward in this analysis.
In the projected results for 2028, EPA identified 358 potentially affected EGUs, owned
by 107 entities. Of these, EPA identified 41 potentially affected EGUs owned by 26 small
entities included in the power sector baseline.
50 Estimates of sales were used in lieu of revenue estimates when revenue data was unavailable.
51 IPM output files report aggregated results for "model" plants (i.e., aggregates of generating units with similar
operating characteristics). Parsed files approximate the IPM results at the generating unit level.
52 The Regulatory Flexibility Act defines a small government jurisdiction as the government of a city, county,
town, township, village, school district, or special district with a population of less than 50,000
(5 U.S.C. section 601(5)). For the purposes of the RFA, States and tribal governments are not
considered small governments. EPA's Final Guidance for EPA Rulewriters: Regulatory Flexibility Act is located
here: https://www.epa.gov/sites/default/files/2015-06/documents/guidance-regflexact.pdf.
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The chosen compliance strategy will be primarily a function of the unit's marginal
control costs and its position relative to the marginal control costs of other units. To attempt to
account for each potential control strategy, EPA estimates compliance costs as follows:
CCompliance A COperating+Retrofit A CFuel A R
where C represents a component of cost as labeled and A R represents the change in revenues,
calculated as the difference in value of electricity generation between the baseline case and the
rule in in 2028.
Realistically, compliance choices and market conditions can combine such that an entity
may actually experience a reduction in any of the individual components of cost. Under the rule,
some units will forgo some level of electricity generation (and thus revenues) to comply, and this
impact will be lessened on these entities by the projected increase in electricity prices under the
rule. On the other hand, those units increasing generation levels will see an increase in electricity
revenues and as a result, lower net compliance costs. If entities are able to increase revenue more
than an increase in fuel cost and other operating costs, ultimately, they will have negative net
compliance costs (or increased profit). Overall, small entities are not projected to install
relatively costly emissions control retrofits but may choose to do so in some instances. Because
this analysis evaluates the total costs along each of the compliance strategies laid out above for
each entity, it inevitably captures gains such as those described. As a result, what we describe as
cost is actually a measure of the net economic impact of the rule on small entities.
For this analysis, EPA used IPM-parsed output to estimate costs based on the parameters
above, at the unit level. These impacts were then summed for each small entity, adjusting for
ownership share. Net impact estimates were based on the following: operating and retrofit costs,
sale or purchase of allowances, and the change in fuel costs or electricity generation revenues
under the proposed MATS requirements relative to the base case. These individual components
of compliance costs were estimated as follows:
1. Operating and retrofit costs (A Coperamg+Retrofu)'. EPA projected which compliance
option would be selected by each EGU in 2028 and applied the appropriate cost to this
choice (for details, please see Section 3 of this RIA). For 2028, IPM projected retrofit
costs were also included in the calculation.
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2. Fuel costs (A CFuei): The change in fuel expenditures under the proposed requirements
was estimated by taking the difference in projected fuel expenditures between the IPM
estimates under the proposed requirements and the baseline.
3. Value of electricity generated (A To estimate the value of electricity generated,
the projected level of electricity generation is multiplied by the regional-adjusted retail
electricity price ($/MWh) estimate, for all entities except those categorized as private in
Ventyx. See Section 3 for a discussion of the Retail Price Model, which was used to
estimate the change in the retail price of electricity. For private entities, EPA used the
wholesale electricity price instead of the retail electricity price because most of the
private entities are independent power producers (IPP). IPPs sell their electricity to
wholesale purchasers and do not own transmission facilities. Thus, their revenue was
estimated with wholesale electricity prices.
5.2.2 Results
As indicated above, the use of a sales test for estimating small business impacts for a
rulemaking is consistent with guidance offered by the EPA on compliance with the RFA and is
consistent with guidance published by the SBA's Office of Advocacy that suggests that cost as a
percentage of total revenues is a metric for evaluating cost increases on small entities in relation
to increases on large entities. EPA assessed the economic and financial impacts of the rule using
the ratio of compliance costs to the value of revenues from electricity generation, focusing in
particular on entities for which this measure is greater than 1 percent.
The projected impacts, including compliance costs, of the proposed rule on small entities
are summarized in Table 5-25-2. All costs are presented in 2019 dollars. We projected the annual
net compliance cost to small entities to be approximately $26 million in 2028. Relative to the
baseline, the proposed rule is projected to generate compliance cost reductions greater than 1
percent of baseline revenue for two of the 26 small entities directly impacted, and compliance
cost increases greater than 1 percent are projected for two. The remaining 22 entities are not
projected to experience compliance cost changes of more than 1 percent. Of the 26 entities
considered in this analysis, two are holding units projected to experience compliance cost
increases greater than 1 percent of generation revenue at a facility level as well as at a parent
holding company level.
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Table 5-2 Projected Impacts of Proposal on Small Entities in 2028
EGU
Ownership Type
Number of Potentially
Affected Entities
Total Net Compliance
Cost (millions 2019
dollars)
Number of Small
Entities with
Compliance Costs >1%
of Generation Revenues
Municipal
0
0
0
Private
12
-3.9
1
Co-op
14
30
1
Total
26
26
2
A similar analysis of the projected impacts, including compliance costs, of the more
stringent alternative on small entities is summarized in Table 5-3. We projected annual net
compliance cost to small entities to be approximately -$6.0 million in 2028. Relative to the
baseline, the more stringent alternative is projected to generate compliance cost reductions
greater than 1 percent of baseline revenues for 15 of the 26 entities directly impacted, and
compliance cost increases greater than 1 percent are projected for three. The remaining eight
small entities are not projected to experience compliance cost changes of more than 1 percent.
Table 5-3 Pro jected Impacts of More Stringent Alternative on Small Entities in 2028
EGU
Ownership Type
Number of Potentially
Affected Entities
Total Net Compliance
Cost (millions 2019
dollars)
Number of Small
Entities with
Compliance Costs >1%
of Generation Revenues
Municipal
0
0
0
Private
12
-62
0
Co-op
14
56
3
Total
26
-6.0
3
5.2.3 Conclusion
Making a determination that there is not a significant economic impact on a substantial
number of small entities (often referred to as a "SISNOSE") requires an assessment of whether
an estimated economic impact is significant and whether that impact affects a substantial number
of small entities. The analysis indicates that 8 small entities see a +/- 1 percent change in either
emissions, fuel use, or generation, and 3 of these are projected to have a cost impact of greater
than 1 percent of their revenues. EPA identified 107 potentially affected EGU entities in the
projection year of 2028. Of these, EPA identified 26 small entities affected by the rule, and of
these, three small entities may experience costs of greater than 1 percent of revenues. Based on
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this analysis, for this rule overall we conclude that the estimated costs for the proposed rule will
not have a significant economic impact on a substantial number of small entities.
5.3 Labor Impacts
This section discusses potential employment impacts of this regulation. As economic
activity shifts in response to a regulation, typically there will be a mix of declines and gains in
employment in different parts of the economy over time and across regions. To present a
complete picture, an employment impact analysis will describe the potential positive and
negative changes in employment levels. There are significant challenges when trying to evaluate
the employment effects of an environmental regulation due to a wide variety of other economic
changes that can affect employment, including the impact of the coronavirus pandemic on labor
markets and the state of the macroeconomy generally. Considering these challenges, we look to
the economics literature to provide a constructive framework and empirical evidence. To
simplify, we focus on impacts on labor demand related to compliance behavior. Environmental
regulation may also affect labor supply through changes in worker health and productivity (Zivin
and Neidell, 2018).
Economic theory of labor demand indicates that employers affected by environmental
regulation may increase their demand for some types of labor, decrease demand for other types,
or for still other types, not change their demand at all(Berman and Bui, 2001; Deschenes, 2018;
Morgenstern et al., 2002). To study labor demand impacts empirically, a growing literature has
compared employment levels at facilities subject to an environmental regulation to employment
levels at similar facilities not subject to that environmental regulation; some studies find no
employment effects, and others find significant differences. For example, see Berman and Bui
(2001), Greenstone (2002), Ferris et al. (2014), and Curtis (2018, 2020). A variety of conditions
can affect employment impacts of environmental regulation, including baseline labor market
conditions and employer and worker characteristics such as occupation and industry. Changes in
employment may also occur in different sectors related to the regulated industry, both upstream
and downstream, or in sectors producing substitute or complimentary products. Employment
impacts in related sectors are often difficult to measure. Consequently, we focus our labor
impacts analysis primarily on the directly regulated facilities and other EGUs and related fuel
markets.
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This section discusses and projects potential employment impacts for the utility power,
coal and natural gas production sectors that may result from the proposed rule. EPA has a long
history of analyzing the potential impacts of air pollution regulations on changes in the amount
of labor needed in the power generation sector and directly related sectors. The analysis
conducted for this RIA builds upon the approaches used in the past and takes advantage of newly
available data to improve the assumptions and methodology.53
The results presented in this section are based on a methodology that estimates the impact
on employment based on the differences in projections between two modeling scenarios: the
baseline scenario, and a scenario that represents the implementation of the rule. The estimated
employment difference between these scenarios can be interpreted as the incremental effect of
the rule on employment in this sector. As discussed in Section 3, there is uncertainty related to
the future baseline projections. Because the incremental employment estimates presented in this
section are based on projections discussed in Section 3, it is important to highlight the relevance
of the Section 3 uncertainty discussion to the analysis presented in this section. Note that there is
also uncertainty related to the employment factors applied in this analysis, particularly factors
informing job-years related to relatively new technologies, such as energy storage, on which
there is limited data to base assumptions.
Like previous analyses, this analysis represents an evaluation of "first-order employment
impacts" using a partial equilibrium modeling approach. It includes some of the potential ripple
effects of these impacts on the broader economy. These ripple effects include the secondary job
impacts in both upstream and downstream sectors. The analysis includes impacts on upstream
sectors including coal, natural gas, and uranium. However, the approach does not analyze
impacts on other fuel sectors, nor does it analyze potential impacts related to transmission or
distribution. This approach excludes the economy-wide employment effects of changes to energy
markets (such as higher or lower forecasted electricity prices). This approach also excludes labor
impacts that are sometimes reflected in a benefits analysis for an environmental policy, such as
increased productivity from a healthier workforce and reduced absenteeism due to fewer sick
days of employees and dependent family members (e.g., children).
53 For a detailed overview of this methodology, including all underlying assumptions, see the U.S. EPA
Methodology for Power Sector-Specific Employment Analysis, available in the docket.
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5.3.1 Overview of Methodology
The methodology includes the following two general approaches, based on the available
data. The first approach utilizes the rich employment data that is available for several types of
generation technologies in the 2020 U.S. Energy and Employment Report.54 For employment
related to other electric power sector generating and pollution control technologies, the second
approach utilizes information available in the U.S. Economic Census.
Detailed employment inventory data is available regarding recent employment related to
coal, hydro, natural gas, geothermal, wind, and solar generation technologies. The data enables
the creation of technology-specific factors that can be applied to model projections of capacity
(reported in megawatts, or MW) and generation (reported in megawatt-hours, or MWh) in order
to estimate impacts on employment. Since employment data is only available in aggregate by
fuel type, it is necessary to disaggregate by labor type in order to differentiate between types of
jobs or tasks for categories of workers. For example, some types of employment remain constant
throughout the year and are largely a function of the size of a generator, e.g., fixed operation and
maintenance activities, while others are variable and are related to the amount of electricity
produced by the generator, e.g., variable operation and maintenance activities.
The approach can be summarized in three basic steps:
• Quantify the total number of employees by fuel type in a given year;
• Estimate total fixed operating & maintenance (FOM), variable operating &
maintenance (VOM), and capital expenditures by fuel type in that year; and
• Disaggregate total employees into three expenditure-based groups and develop factors
for each group (FTE/MWh, FTE/MW-year, FTE/MW new capacity).
Where detailed employment data is unavailable, it is possible to estimate labor impacts
using labor intensity ratios. These factors provide a relationship between employment and
economic output and are used to estimate employment impacts related to construction and
operation of pollution control retrofits, as well as some types of electric generation technologies.
54 https://www.usenergyjobs.org/
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For a detailed overview of this methodology, including all underlying assumptions and
the types of employment represented by this analysis, see the U.S. EPA Methodology for Power
Sector-Specific Employment Analysis, available in the docket.
5.3.2 Overview of Power Sector Employment
In this section we focus on employment related to electric power generation, as well as
coal and natural gas extraction because these are the segments of the power sector that are most
relevant to the projected impacts of the rule. Other segments not discussed here include other
fuels, energy efficiency, and transmission, distribution, and storage. The statistics presented here
are based on the 2020 USEER, which reports data from 2019.55
In 2019, the electric power generation sector employed nearly 900,000 people. Relative
to 2018, this sector grew by over 2 percent, despite job losses related to nuclear and coal
generation. These losses were offset by increases in employment related to other generating
technologies, including natural gas, solar, and wind. The largest component of total 2019
employment in this sector is construction (33 percent). Other components of the electric power
generation workforce include: utility workers (20 percent), professional and business service
employees (20 percent), manufacturing (13 percent), wholesale trade (8 percent), and other (5
percent). In 2019, jobs related to solar and wind generation represent 31 percent and 14 percent
of total jobs, respectively, and jobs related to coal generation represent 10 percent of total
employment.
In addition to generation-related employment we also look at employment related to coal
and natural gas use in the electric power sector. In 2019, the coal industry employed about
75,000 workers. Mining and extraction jobs represent the vast majority of total coal-related
employment in 2019 (74 percent). The natural gas fuel sector employed about 276,000
employees in 2019. About 60 percent of those jobs were related to mining and extraction.
55 While 2020 data is available in the 2021 version of this report, this section of the RIA utilizes 2019 data because
this year does not reflect any short-term trends related to the COVID-19 pandemic. The annual report is available
at: https://www.usenergyjobs.org/.
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5.3.3 Projected Sectoral Employment Changes due to the Proposed Rule
Electric generating units subject to the mercury and filterable PM emission limits in this
proposed rule will likely use various mercury and PM control strategies to comply. Under the
modeling of the proposed rule, about 2 GW of coal capacity is estimated to install ESP upgrades,
and about 3 GW of coal capacity is estimated to either upgrade existing fabric filters or construct
new fabric filter controls by 2028. Additionally, the proposed rule is projected to result in an
additional 500 MW of retired coal capacity (less than one percent) in 2028, and small increase in
new natural gas and energy storage capacity (each significantly less than 1 GW and less than 1
percent) in that year.
Based on these power sector modeling projections, we estimate an increase in
construction-related job-years related to the installation of new pollution controls under the rule,
as well as the construction of new generating capacity. In 2028, we estimate an increase of
approximately 800 construction-related job-years related to the construction of new pollution
controls. We estimate an increase of over 20,00 job-years in 2028 related to the construction of
new capacity in that year. In 2030 and 2035, we estimate decreases in construction-related job-
years. This near-term increase followed by subsequent decreases results from the projected
acceleration of a small amount of new capacity that is projected to be built in the baseline in
2030 and beyond. Construction-related job-year changes are one-time impacts, occurring during
each year of the multi-year periods during which construction of new capacity is completed.
Construction-related figures in Table 5-3 represent a point estimate of incremental changes in
construction jobs for each year (for a three-year construction projection, this table presents one-
third of the total jobs for that project).
Table 5-3 Changes in Labor Utilization: Construction-Related (Number of Job-Years
of Employment in a Single Year)
2028
2030
2035
New Pollution Controls
800
<100
<100
New Capacity
20,600
-8,700
-500
Notes: "<100" denotes an increase or decrease of less than 100 job-years; A large share of the construction-related
job years is attributable to construction of energy storage, a relatively new technology on which there is limited data
to base labor assumptions.
We also estimate changes in the number of job-years related to recurring non-
construction employment. Recurring employment changes are job-years associated with annual
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recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly
built generating capacity creates a recurring stream of positive job-years, while retiring
generating capacity, as well as avoided capacity builds, create a stream of negative job-years.
The rule is projected to result, generally, in a replacement of relatively labor-intensive coal
capacity with less labor-intensive capacity, which results in an overall decrease of non-
construction jobs in 2028 and 2030. The total net estimated decrease in recurring employment is
about 300 job-years in over 2028-2035, which is a very small percentage of total 2019 power
sector employment reported in the 2020 USEER (approximately 900,000 generation-related jobs,
75,000 coal-related jobs, and 276,000 natural gas-related jobs). Table 5-4 provide detailed
estimates of recurring non-construction employment changes.
Table 5-4 Changes in Labor Utilization: Recurring Non-Construction (Number of Job-
Years of Employment in a Single Year)
2028
2030
2035
Pollution Controls
<100
<100
<100
Existing Capacity
-200
-200
-200
New Capacity
<100
<100
300
Fuels (Coal, Natural Gas, Uranium)
<100
<100
<100
Coal
<100
<100
<100
Natural Gas
<100
<100
<100
Uranium
<100
<100
<100
Note: "<100" denotes an increase or decrease of less than 100 job-years; Numbers may not sum due to rounding
5.3.4 Conclusions
Generally, there are significant challenges when trying to evaluate the employment
effects due to an environmental regulation from employment effects due to a wide variety of
other economic changes, including the impact of the coronavirus pandemic on labor markets and
the state of the macroeconomy generally. For EGUs, this proposed rule may result in a sizable
near-term increase in construction-related jobs related to the installation of new pollution
controls, as well as the acceleration of small amounts of new generating capacity construction.
The rule is also projected to result, generally, in a replacement of relatively labor-intensive coal
capacity with less labor-intensive capacity (primarily solar), which results in an overall decrease
of non-construction jobs. Speaking generally, a variety of federal programs are available to
invest in communities potentially affected by coal mine and coal power plant closures. An initial
report by The Interagency Working Group on Coal and Power Plant Communities and Economic
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Revitalization (April 2021) identifies funding available to invest in such "energy communities"
through existing programs from agencies including Department of Energy, Department of
Treasury, Department of Labor, and others.56 The Inflation Reduction Act also provides
incentives to encourage investment in communities affected by coal mine and coal power plant
closures.57
5.4 References
Berman, E., & Bui, L. T. M. (2001). Environmental regulation and labor demand: evidence from
the South Coast Air Basin. Journal of Public Economics, 79(2), 265-295.
doi -.https://doi.org/10.1016/S0047-2 72 7(99)00101-2
Curtis, E. M. (2018). Who Loses under Cap-and-Trade Programs? The Labor Market Effects of
the NOx Budget Trading Program. The Review of Economics and Statistics, 100(1), 151-
166. doi: 10.1162/REST_a_00680
Curtis, E. M. (2020). Reevaluating the ozone nonattainment standards: Evidence from the 2004
expansion. Journal of Environmental Economics and Management, 99, 102261.
doi: 10.1016/j.jeem.2019.102261
Deschenes, O. (2018). Environmental regulations and labor markets. IZA World of Labor, 22.
doi: 10.1518 5/izawol. 22. v2
Ferris, A. E., Shadbegian, R. J., & Wolverton, A. (2014). The Effect of Environmental
Regulation on Power Sector Employment: Phase I of the Title IV S02 Trading Program.
Journal of the Association of Environmental and Resource Economists, 7(4), 521-553.
doi: 10.1086/679301
Greenstone, M. (2002). The Impacts of Environmental Regulations on Industrial Activity:
Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of
Manufactures. Journal of Political Economy, 110(6), 1175-1219. doi: 10.1086/342808
Morgenstern, R. D., Pizer, W. A., & Shih, J.-S. (2002). Jobs Versus the Environment: An
Industry-Level Perspective. Journal of Environmental Economics and Management,
43(3), 412-436. doi:https://doi.org/10.1006/jeem.2001.1191
Zivin, J. G., & Neidell, M. (2018). Air pollution's hidden impacts. Science, 359(6371), 39-40.
doi:doi: 10.1126/science.aap7711
56 See "Initial Report to the President on Empowering Workers Through Revitalizing Energy Communities" April
2021 at https://energycommunities.gOv/wp-content/uploads/2021/l 1/Initial-Report-on-Energy-
Communities_Apr2021 .pdf
57 For more details see Congressional Research Service. "Inflation Reduction Act of 2022 (IRA): Provisions
Related to Climate Change" October 3, 2022 at https://crsreports.congress.gOv/product/pdf/R/R47262
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6 ENVIRONMENTAL JUSTICE IMPACTS
6.1 Introduction
E.O. 12898 directs EPA to "achiev[e] environmental justice (EJ) by identifying and
addressing, as appropriate, disproportionately high and adverse human health or environmental
effects" (59 FR 7629, February 16, 1994), termed disproportionate impacts in this section.
Additionally, E.O. 13985 was signed to advance racial equity and support underserved
communities through Federal government actions (86 FR 7009, January 20, 2021). EPA defines
EJ as the fair treatment and meaningful involvement of all people regardless of race, color,
national origin, or income with respect to the development, implementation, and enforcement of
environmental laws, regulations, and policies. EPA further defines the term fair treatment to
mean that "no group of people should bear a disproportionate burden of environmental harms
and risks, including those resulting from the negative environmental consequences of industrial,
governmental, and commercial operations or programs and policies."58 Meaningful involvement
means that: (1) potentially affected populations have an appropriate opportunity to participate in
decisions about a proposed activity that will affect their environment and/or health; (2) the
public's contribution can influence the regulatory Agency's decision; (3) the concerns of all
participants involved will be considered in the decision-making process; and (4) the rule-writers
and decision-makers seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that are
extensive enough that they may merit Agency action.59 In general, the determination of whether a
disproportionate impact exists is ultimately a policy judgment which, while informed by
analysis, is the responsibility of the decision-maker. The terms "difference" or "differential"
indicate an analytically discernible distinction in impacts or risks across population groups. It is
the role of the analyst to assess and present differences in anticipated impacts across population
groups of concern for both the baseline and proposed regulatory options, using the best available
information (both quantitative and qualitative) to inform the decision-maker and the public.
58 See, e.g., "Environmental Justice." Epa.gov, U.S. Environmental Protection Agency, 4 Mar. 2021,
https://www.epa.gov/environmentaljustice^
59 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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A regulatory action may involve potential EJ concerns if it could: (1) create new
disproportionate impacts on minority populations, low-income populations, and/or Indigenous
peoples; (2) exacerbate existing disproportionate impacts on minority populations, low-income
populations, and/or Indigenous peoples; or (3) present opportunities to address existing
disproportionate impacts on minority populations, low-income populations, and/or Indigenous
peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223;
January 20, 2021) calls for procedures to "take into account the distributional consequences of
regulations, including as part of a quantitative or qualitative analysis of the costs and benefits of
regulations, to ensure that regulatory initiatives appropriately benefit, and do not inappropriately
burden disadvantaged, vulnerable, or marginalized communities." Under E.O. 13563, federal
agencies may consider equity, human dignity, fairness, and distributional considerations, where
appropriate and permitted by law. For purposes of analyzing regulatory impacts, EPA relies upon
its June 2016 "Technical Guidance for Assessing Environmental Justice in Regulatory
Analysis,"60 which provides recommendations that encourage analysts to conduct the highest
quality analysis feasible, recognizing that data limitations, time, resource constraints, and
analytical challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is to
review the available evidence from the published literature and from community input on what
factors may make population groups of concern more vulnerable to adverse effects (e.g.,
underlying risk factors that may contribute to higher exposures and/or impacts). It is also
important to evaluate the data and methods available for conducting an EJ analysis. EJ analyses
can be grouped into two types, both of which are informative, but not always feasible for a given
rulemaking:
1. Baseline: Describes the current (pre-control) distribution of exposures and risk,
identifying potential disparities.
2. Policy: Describes the distribution of exposures and risk after the regulatory option(s)
have been applied (post-control), identifying how potential disparities change in
response to the rulemaking.
60 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or
methodology for conducting EJ analyses, though a key consideration is consistency with the
assumptions underlying other parts of the regulatory analysis when evaluating the baseline and
regulatory options.
6.2 Analyzing E J Impacts in This Proposal
In addition to the benefits assessment (see Section 4), EPA considers potential EJ
concerns associated with this proposed rulemaking. A potential EJ concern is defined as "the
actual or potential lack of fair treatment or meaningful involvement of minority populations,
low-income populations, tribes, and Indigenous peoples in the development, implementation and
enforcement of environmental laws, regulations and policies."61 For analytical purposes, this
concept refers more specifically to "disproportionate impacts on minority populations, low-
income populations, and/or Indigenous peoples that may exist prior to or that may be created by
the proposed regulatory action." Although EJ concerns for each rulemaking are unique and
should be considered on a case-by-case basis, EPA's EJ Technical Guidance states that "[t]he
analysis of potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern in the baseline?
2. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern for the regulatory option(s) under
consideration?
3. For the regulatory option(s) under consideration, are potential EJ concerns created [,
exacerbated,] or mitigated compared to the baseline?"
To address these questions, EPA developed an analytical approach that considers the
purpose and specifics of the rulemaking, as well as the nature of known and potential exposures
across various demographic groups. While the proposal targets HAP emissions, other local air
pollutants emissions may also be reduced, such as NOx and SO2. These emissions can lead to
localized exposures that may be associated with health effects in nearby populations at
61 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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sufficiently high concentrations and certain populations may be at increased risk of exposure-
related health effects, such as people with asthma.
As HAP exposure results generated as part of the 2020 Residual Risk analysis were
below both the presumptive acceptable cancer risk threshold and the noncancer health
benchmarks, and this proposed regulation should further reduce exposure to HAP, there are no
'disproportionate and adverse effects' of potential EJ concern. Therefore, we did not perform a
quantitative EJ assessment of HAP risk. In addition, technical limitations prevented analysis of
NOx and SO2 emission reductions. While HAP, NO2, and SO2 exposures and concentrations
were not directly evaluated as part of this EJ assessment, due to the potential for reductions in
these and other environmental stressors nearby affected sources, EPA qualitatively discussed EJ
impacts of HAP (Section 6.3) and conducted a proximity analysis to evaluate the potential EJ
implications of changes in localized exposures (Section 6.4).62
As this proposed rule is also expected to reduce ambient PM2.5 and ozone concentrations,
EPA conducted a quantitative analysis of modeled changes in PM2.5 and ozone concentrations
across the continental U.S. resulting from the control strategies projected to occur under the rule,
characterizing aggregated and distributional exposures both prior to and following
implementation of the proposed regulatory and more stringent regulatory options in 2028, 2030,
and 2035 (Section 6.5).
Unique limitations and uncertainties are specific to each type of analysis, which are
described prior to presentation of analytic results in the subsections below.
6.3 Qualitative Assessment of HAP Impacts
As required by Section 112(n)(l)(A) of the Clean Air Act, the EPA has determined that it
is appropriate and necessary to regulate HAP emissions from coal- and oil-fired EGUs. This
determination is driven by the significant public health risks and harms posed by these emissions
as evaluated against the availability and costs of emissions controls that could be employed to
reduce this harmful pollution. As part of the appropriate and necessary determination, the
Administrator specifically considered the impacts of EGU HAP emissions on different
62 The 2016 NOx ISA and 2017 Sox ISA identified people with asthma, children, and older adults as being at
increased risk of NO2- and SO2- related health effects and the 2017 SOx ISA.
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populations and concluded that certain parts of the U.S. population may be especially vulnerable
to mercury emissions based on their characteristics or circumstances. In some cases, the
enhanced vulnerability relates to life stage (e.g., fetuses, infants, young children). In other cases,
the enhanced vulnerability can be ascribed to the communities in which the population lives.
Higher cumulative levels of pollution are often associated with areas affected by past and present
environmental injustice. In this second category, the greater sensitivity to HAP emissions can be
attributed to poorer levels of overall health (e.g., higher rates of cardiovascular disease,
nutritional deficiencies) or to dietary practices which are more common in low-income
communities of color (e.g., subsistence fishers). The net effect is that certain sub-populations
may be especially vulnerable to EGU HAP emissions and that these emissions are a potential EJ
concern.
Of the HAP potentially impacted by this proposed rulemaking, mercury is a persistent
and bioaccumulative toxic metal that can be readily transported and deposited to soil and aquatic
environments where it is transformed by microbial action into methylmercury.63 Consumption of
fish is the primary pathway for human exposure to methylmercury. Methylmercury
bioaccumulates in the aquatic food web eventually resulting in highly concentrated levels of
methylmercury within larger fish.64 ANAS Study reviewed the effects of methylmercury on
human health and concluded that it is highly toxic to multiple human and animal organ systems.
Of particular concern is chronic prenatal exposure via maternal consumption of foods containing
methylmercury. Elevated exposure has been associated with developmental neurotoxicity and
manifests as poor performance on neurobehavioral tests, particularly on tests of attention, fine
motor function, language, verbal memory, and visual-spatial ability. Because the impacts of the
neurodevelopmental effects of methylmercury are greatest during periods of rapid brain
development, developing fetuses, infants, and young children are particularly vulnerable. In
particular, children born to populations with high fish consumption (e.g., people consuming fish
as a dietary staple) or impaired nutritional status may be especially susceptible to adverse
neurodevelopmental outcomes. As part of the 2023 Final A&N Review, EPA evaluated how the
neurodevelopmental and cardiovascular risks varied across populations. That analysis completed
63 U.S. EPA. 1997. Mercury Study Report to Congress. EPA-452/R-97-003 December 1997.
64 National Research Council (NAS). 2000. Toxicological Effects of Methylmercury. Committee on the
Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology, National Research
Council.
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in support of the appropriate and necessary determination (addressing the EGU sector
collectively) suggested that subsistence fisher populations that are racially, culturally,
geographically, and/or income-differentiated could experience elevated exposures relative to not
only the general population but also the population of subsistence fishers generally. As noted in
Section 4 of this document, while previous EPA studies have shown that current modeled
exposures are well below the RfD, we conclude that further reductions in mercury emissions
from lignite-fired EGUs covered in this proposed action should further reduce exposures for the
subsistence fisher sub-population. However, as we do not expect appreciable adverse health
effects as a result of HAP emissions from this source category we have not conducted
quantitative or qualitative analyses to assess specific mercury-related impacts of this action for
EJ communities of potential concern or how those impacts differ from U.S. population-wide
effects.
6.4 Demographic Proximity Analyses of Existing Facilities
Demographic proximity analyses allow one to assess the potentially vulnerable
populations residing near affected facilities as a proxy for exposure and the potential for adverse
health impacts that may occur at a local scale due to economic activity at a given location
including noise, odors, traffic, and emissions such as NO2 and SO2 covered under this EPA
action and not modeled elsewhere in this RIA.
Although baseline proximity analyses are presented here, several important caveats
should be noted. Emissions are expected to both decrease and increase from the rulemaking in
the three modeled future years, so communities near affected facilities could experience either
improvements or worsening in air quality from directly emitted pollutants. It should also be
noted that facilities may vary widely in terms of the impacts they already pose to nearby
populations. In addition, proximity to affected facilities does not capture variation in baseline
exposure across communities, nor does it indicate that any exposures or impacts will occur and
should not be interpreted as a direct measure of exposure or impact. These points limit the
usefulness of proximity analyses when attempting to answer questions from EPA's EJ Technical
Guidance.
Demographic proximity analyses were performed for all plants with at least one coal-
fired unit greater than 25 MW without retirement or gas conversion plans before 2029 affected
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by this proposed rulemaking. Due to the distinct regulatory requirements, the following subsets
of affected facilities were separately evaluated:
• Lignite plants (12 facilities) with units potentially subject to the proposed mercury
standard revision: Comparison of the percentage of various populations (race/ethnicity,
age, education, poverty status, income, and linguistic isolation) living near the facilities to
average national levels.
• Coal plants (12 facilities) with units potentially subject to the proposed filterable PM
standard revision: Comparison of the percentage of various populations (race/ethnicity,
age, education, poverty status, income, and linguistic isolation) living near the facilities to
average national levels.
• Coal plants (48 facilities) with units potentially subject to the alternate filterable PM
standard revision: Comparison of the percentage of various populations (race/ethnicity,
age, education, poverty status, income, and linguistic isolation) living near the facilities to
average national levels.
The current analysis identified all census blocks with centroids within a 10 km radius of
the latitude/longitude location of each facility, and then linked each block with census-based
demographic data.65 The total population within a specific radius around each facility is the sum
of the population for every census block within that specified radius, based on each block's
population provided by the 2020 decennial Census.66 Statistics on race, ethnicity, age, education
level, poverty status and linguistic isolation were obtained from the Census' American
Community Survey (ACS) 5-year averages for 2016-2020. These data are provided at the block
group level. For the purposes of this analysis, the demographic characteristics of a given block
group - that is, the percentage of people in different races/ethnicities, the percentage without a
high school diploma, the percentage that are below the poverty level, the percentage that are
below two times the poverty level, and the percentage that are linguistically isolated - are
presumed to also describe each census block located within that block group.
In addition to facility-specific demographics, the demographic composition of the total
population within the specified radius (e.g., 10 km) for all facilities was also computed (e.g., all
EGUs potentially subject to the mercury standard revision). In calculating the total populations,
65 The 10 km distance was determined to be the shortest radius around these units that captured a large enough
population to avoid excessive demographic uncertainty.
66 The location of the Census block centroid is used to determine if the entire population of the Census block is
assumed to be within the specified radius. It is unknown how sensitive these results may be to different methods of
population estimation, such as aerial apportionment.
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to avoid double-counting, each census block population was only counted once. That is, if a
census block was located within the selected radius (i.e., 10 km) for multiple facilities, the
population of that census block was only counted once in the total population. Finally, this
analysis compares the demographics at each specified radius (i.e., 10 km) to the demographic
composition of the nationwide population.
Table 6-1 shows the results of the proximity analysis for the three sets of affected
facilities investigated. The analysis indicates that, on average, the percentage of the population
living within 10 km of these units that is African American, Hispanic/Latino, and
Other/Multiracial is significantly lower than the national average. One exception is the percent of
the population that is Native American within 10 km of the lignite plants (0.9 percent) that is
above the national average (0.6 percent). This is driven by four facilities that have a percent
Native American population living within 10 km ranging from 1.3 percent up to 5.9 percent.
Also, on average, the populations living within 10 km of the units subject to the proposed or
alternate filterable PM standards have a higher percentage of people living below two times the
poverty level than the national average (30 to 33 percent versus 29 percent).
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Table 6-1 Proximity Demographic Assessment Results Within 10 km of Coal-Fired
Units Greater than 25 MW Without Retirement or Gas Conversion Plans Before 2029
Affected by this Proposed Rulemaking a'b
Population within 10 km
Demographic Group
Lignite plants
potentially subject to
Nationwide Average proposed mercury
for Comparison standard
Coal plants
potentially subject to
proposed filterable
PM standard
Coal plants
potentially subject to
alternate filterable
PM standard
Total Population
Number of Facilities
329,824,950
17,790
12
233,575
12
854,120
48
Race and Ethnicity by Percent
White
African American
Native American
Hispanic or Latino2
Other and Multiracial
60%
12%
0.60%
19%
9%
79%
12%
0.9%
5%
2%
80%
4%
0.40%
12%
3%
74%
6%
0.40%
15%
4%
Income by Percent
Below Poverty Level
13%
12%
14%
13%
Below 2x Poverty
Level
29%
28%
33%
30%
Education by Percent
>25 and w/o a HS
Diploma
12%
13%
13%
11%
Linguistically Isolated by Percent
Linguistically Isolated
5%
2%
3%
2%
a The nationwide population count and all demographic percentages are based on the Census' 2016-2020 American
Community Survey five-year block group averages and include Puerto Rico. Demographic percentages based on
different averages may differ. The total population counts are based on the 2020 Decennial Census block
populations.
b To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these
analyses. A person is identified as one of five racial/ethnic categories above: White, African American, Native
American, Other and Multiracial, or Hispanic/Latino. A person who identifies as Hispanic or Latino is counted as
Hispanic/Latino for this analysis, regardless of what race this person may have also identified as in the Census.
Includes white and nonwhite.
6.5 E J PM2.5 and Ozone Exposure Impacts
This EJ air pollutant exposure67 analysis aims to evaluate the potential for EJ concerns
related to PM2.5 and ozone exposures68 among potentially vulnerable populations. To assess EJ
ozone and PM2.5 exposure impacts, we focus on the first and third of the three EJ questions from
67 The term exposure is used here to describe estimated PM2 5 and ozone concentrations and not individual dosage.
68 Air quality surfaces used to estimate exposures are based on 12 km2 grids. Additional information on air quality
modeling can be found in the air quality modeling information section.
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the EPA's 2016 EJ Technical Guidance,69 which ask if there are potential EJ concerns associated
with stressors affected by the regulatory action for population groups of concern in the baseline
and if those potential EJ concerns in the baseline are exacerbated, unchanged, or mitigated under
the regulatory options being considered.70
To address these questions with respect to the PM2.5 and ozone exposures, EPA
developed an analytical approach that considers the purpose and specifics of this proposed
rulemaking, as well as the nature of known and potential exposures and impacts. Specifically, as
1) this proposed rule affects EGUs across the U.S., which typically have tall stacks that result in
emissions from these sources being dispersed over large distances, and 2) both ozone and PM2.5
can undergo long-range transport, it is appropriate to conduct an EJ assessment of the contiguous
U.S. Given the availability of modeled PM2.5 and ozone air quality surfaces under the baseline
and proposed regulatory options, we conduct an analysis of changes in PM2.5 and ozone
concentrations resulting from the emission changes projected by IPM71 to occur under the
proposed rule as compared to the baseline scenario, characterizing average and distributional
exposures following implementation of the proposed regulatory options in the implementation
year (2028), 2030, and 2035. However, several important caveats of this analysis are as follows:
69 U.S. Environmental Protection Agency (EPA), 2015. Guidance on Considering Environmental Justice During the
Development of Regulatory Actions, https://www.epa.gov/sites/default/files/2015-06/documents/considering-ej-in-
rulemaking-guide-final.pdf
70 EJ question 2, which asks if there are potential EJ concerns (i.e., disproportionate burdens across population
groups) associated with environmental stressors affected by the regulatory action for population groups of concern
for the regulatory options under consideration, was not focused on for several reasons. Importantly, the total
magnitude of differential exposure burdens with respect to ozone and PM2 5 among population groups at the national
scale has been fairly consistent pre- and post-policy implementation across recent rulemakings. As such, differences
in nationally aggregated exposure burden averages between population groups before and after the rulemaking tend
to be very similar. Therefore, as disparities in pre- and post-policy burden results appear virtually indistinguishable,
the difference attributable to the rulemaking can be more easily observed when viewing the change in exposure
impacts, and as we had limited available time and resources, we chose to provide quantitative results on the pre-
policy baseline and policy-specific impacts only, which related to EJ questions 1 and 3. We do however use the
results from questions 1 and 3 to gain insight into the answer to EJ question 2 in the summary (Section 6.7).
71 As discussed in greater detail in Section 3, IPM is a comprehensive electricity market optimization model that can
evaluate the impacts of regulatory actions affecting the power sector within the context of regional and national
electricity markets. IPM generates least-cost resource dispatch decisions based on user-specified constraints such as
environmental, demand, and other operational constraints. IPM uses a long-term dynamic linear programming
framework that simulates the dispatch of generating capacity to achieve a demand-supply equilibrium on a seasonal
basis and by region. The model computes optimal capacity that combines short-term dispatch decisions with long-
term investment decisions. IPM runs under the assumption that electricity demand must be met and maintains a
consistent expectation of future load. IPM outputs include the air emissions resulting from the simulated generation
mix.
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• The baseline scenarios for 2028, 2030 and 2035 represent EGU emissions expected in
2028, 2030 and 2035 respectively, but emissions from all other sources are projected to
the year 2026. The 2028, 2030 and 2035 baselines therefore do not capture any
anticipated changes in ambient ozone and PM2.5 between 2026 and 2028, 2030 or 2035
that would occur due to emissions changes from sources other than EGUs.
• Modeling of post-policy air quality concentration changes are based on state-level
emission data paired with facility-level baseline 2026 emissions that were available in the
summer 2021 version of IPM. While the baseline spatial patterns represent ozone and
PM2.5 concentrations associated with the facility level emissions described above, the
post-policy air quality surfaces will capture expected ozone and PM2.5 changes that result
from state-to-state emissions changes but will not capture heterogenous changes in
emissions from multiple facilities within a single state.
• Air quality simulation input information are at a 12 km2 grid resolution and population
information is either at the Census tract- or county-level, potentially masking impacts at
geographic scales more highly resolved than the input information.
• The two specific air pollutant metrics evaluated in this assessment, warm season
maximum daily eight-hour ozone average concentrations and average annual PM2.5
concentrations, are focused on longer-term exposures that have been linked to adverse
health effects. This assessment does not evaluate disparities in other potentially health-
relevant metrics, such as shorter-term exposures to ozone and PM2.5.
• PM2.5 EJ impacts were limited to exposures, and do not extend to health effects, given
additional uncertainties associated with estimating health effects stratified by
demographic population and the ability to predict differential PIVh.s-attributable EJ health
impacts.
Population variables considered in this EJ exposure assessment include race, ethnicity,
educational attainment, employment status, health insurance status, linguistic isolation,
poverty status, age, and sex (Table 6-2).72
72 Population projections stratified by race/ethnicity, age, and sex are based on economic forecasting models
developed by Woods and Poole (2015). The Woods and Poole database contains county-level projections of
population by age, sex, and race out to 2050, relative to a baseline using the 2010 Census data. Population
projections for each county are determined simultaneously with every other county in the U.S to consider patterns of
economic growth and migration. County-level estimates of population percentages within the poverty status and
educational attainment groups were derived from 2015-2019 5-year average ACS estimates. Additional information
can be found in Appendix J of the BenMAP-CE User's Manual (https://www.epa.gov/benmap/benmap-ce-manual-
and-appendices).
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Table 6-2 Demographic Populations Included in the PM2.5 and Ozone EJ Exposure
Analyses
Demographic
Groups
Ages
Spatial Scale of
Population Data
Race
Asian; American Indian; Black; White
0-99
Census tract
Ethnicity
Hispanic; Non-Hispanic
0-99
Census tract
Educational
Attainment
High school degree or more; No high school degree
25-99
Census tract
Employment
Status
Employed; Unemployed; Not in the labor force
0-99
County
Health Insurance
Insured; Uninsured
0-64
County
Speaks English "very well" or better; Speaks English less
Linguistic
Isolation
than "very well" OR
Speaks English "well" or better; Speaks English less than
"well"
0-99
Census tract
Poverty Status
Above the poverty line; Below the poverty line OR
Above 2x the poverty line; Below 2x the poverty line
0-99
Census tract
Children
0-17
Age
Adults
Older Adults
18-64
65-99
Census tract
Sex
Female; Male
0-99
Census tract
6.5.1 Populations Predicted to Experience PM2.5 and Ozone Air Quality Changes
As IPM predicts the proposed rule will lead to both decreases and increases in emissions,
the contiguous U.S. was grouped into areas where air quality 1) improves or does not change, or
2) worsens as a result of the proposed rulemaking. Figure 6-1 shows the average PM2.5 and
ozone concentration in the two above categories for both the proposed and more stringent
regulatory options in each of the three future years for which air quality modeling is available. In
general, the more stringent regulatory option leads to large portions of the population
experiencing greater average PM2.5 and ozone concentration reductions than the proposed policy
option, but also results in portions of the population experiencing greater average PM2.5 and
ozone concentration increases. However, the magnitude of the air pollution exposure changes
from both proposed regulatory options is quite small and somewhat variable across the three
future years analyzed.
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Year Scenario
Pollutant
PM2.5 ((J.g/m3) Ozone (ppb)
2028 Proposal
More Stringent
6M (^352M
0 78M 280mQ
93M^) Z65M
0 14M 345M Q
2030 Proposal
More Stringent
74M (£) 291M
O 98M i (3 267M
12M ^fc353M
9M ° : 355mQ
2035 Proposal
More Stringent
10M £) 370M
8M . j ^372M
0 65M •316M
0 52M 329m(~)
1 1 1
0.000 0.005
Average Change
1 1 1
0.00 0.01 0.02
Average Change
Figure 6-1 Number of People Residing in the Contiguous U.S., Areas Improving or Not
Changing (Teal) or Worsening (Red) in 2028, 2030, and 2035 for PM2.5 and Ozone and the
National Average Magnitude of Pollutant Concentration Changes (jig/m3 and ppb) for the
Proposed and More Stringent Regulatory Options
6.5.2 PM2.5 EJ Exposure Analysis
We evaluated the potential for EJ concerns among potentially vulnerable populations
resulting from exposure to PM2.5 under the baseline and proposed regulatory options in this rule.
This was done by characterizing the distribution of PM2.5 exposures both prior to and following
implementation of the proposed regulatory option, as well as under the more stringent regulatory
option, in 2028, 2030, and 2035.
As this analysis is based on the same PM2.5 spatial fields as the benefits assessment (see
Appendix A for a discussion of the spatial fields), it is subject to similar types of uncertainty (see
Section 4.3.8for a discussion of the uncertainty). A particularly germane limitation for this
analysis is that the expected concentration changes are quite small, likely making uncertainties
associated with the various input data more relevant.
6.5.2.1 National Aggregated Results
National average baseline PM2.5 concentrations in micrograms per cubic meter (|ig/m3) in
2028, 2030, and 2035 are shown in the colored column labeled "baseline" in the Figure 6-2 heat
map. Concentrations in the "baseline" columns represent the total estimated PM2.5 exposure
burden averaged over the 12-month calendar year and are colored to visualize differences more
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easily in average concentrations (lighter blue coloring representing smaller average
concentrations and darker blue coloring representing larger average concentrations). Average
national disparities observed in the baseline of this rule are similar to those described by recent
rules (e.g., the PM NAAQS Proposal), that is, populations with national average PM2.5
concentrations higher than the reference population ordered from most to least difference were:
those linguistically isolated, Hispanics, Asians, Blacks, the less educated, and children. Average
national disparities observed in the baseline of this rule are generally consistent across the three
future years and similar to those described by recent rules (e.g., the PM NAAQS Proposal).
Columns labeled "Proposal" and "More Stringent" provide information regarding how
the proposed regulatory and more stringent options will impact PM2.5 concentrations across
various populations, respectively.73 For all three future years evaluated, there were no discernable
PM2.5 changes under the proposed regulatory option for any population analyzed when showing
concentrations out to the thousandths digit, reiterating the small magnitude of national average
PM2.5 changes. Going to the thousandths digit showed small national-level PM2.5 concentration
reductions for the more stringent regulatory option in all three future years. While the national-
level PM2.5 concentration reductions were identical for all population groups evaluated in 2030
and 2035, there were some differences observed in 2028. For example, on average, the Black
population, which has higher average baseline exposures, is predicted to experience a slightly
greater PM2.5 concentration reduction than the overall reference population. In contrast, the
Asian population, which also has higher average baseline exposures, is estimated to experience a
smaller PM2.5 concentration reduction than the overall reference population.
The national-level assessment of PM2.5 before and after implementation of this proposed
rulemaking suggests that while EJ exposure disparities are present in the pre-policy scenario,
meaningful EJ exposure concerns are not likely created or exacerbated by the rule for the
population groups evaluated, due to the small magnitude of the PM2.5 concentration reductions.
73 We report average exposure results to the decimal place where difference between demographic populations
become visible, as we cannot provide a quantitative estimate of the air quality modeling precision uncertainty. Using
this approach allows for a qualitative consideration of uncertainties and the significance of the relatively small
differences.
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2028
More
Stringent
2030
More
Stringent
2035
More
Stringent
Group
Population
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Reference(0-99)
7.186
0.000
0.006
7.136
0.000
0.001
7 079
0.000
0.002
Race
White (0-99)
7.092
0.000
0.006
7.044
0.000
0.001
6.991
0.000
0.002
American Indian (0-99)
6.716
0.000
0.003
6.681
0.000
0.000
6.644
0.000
0.002
Asian (0-99)
7.788
0.000
0.004
7.719
0.000
0.001
7.631
0.000
0.002
Black(0-99)
7.449
0.000
0 007
7.385
0.000
0.001
7.295
0.000
0.003
Ethnicity
Non-Hispanic (0-99)
6.963
0.000
0 006
6.909
0.000
0.001
6.838
0.000
0.002
Hispanic (0-99)
8.008
0.000
0.005
7.942
0.000
0.001
7.865
0.000
0.002
Educational
More educated (>24: HS or more)
7.089
0.000
0.006
7.039
0.000
0.001
6.984
0.000
0.002
Attainment
Less educated (>24; no HS)
7.530
0.000
0.006
7.478
0.000
0.001
7.432
0.000
0.002
Employment
Status
Employed
Unemployed
Not in the laborforce
7.343
7.186
7.187
0.000
0.000
0.000
0.006
0.006
0.006
7.292
7.136
7.136
0.000
0.000
0.000
0.001
0.001
0.001
7.237
7.079
7.081
0.000
0.000
0.000
0.002
0.002
0.002
Insurance
Status
Insured
Unisured
7.230
7.307
0.000
0.000
0.006
0.006
7.181
7.258
0.000
0.000
0.001
0.001
7 122
7.203
0.000
0.000
0.002
0.003
Linguistic
English "well or better" (0-99)
7,140
0.000
0.006
7.090
0.000
0.001
7.032
0.000
0.002
Isolation
English < "well" (0-99)
8.154
0.000
0.005
8.099
0.000
0.001
8 054
0.000
0.002
Poverty
>Povertyline (0-99)
7.155
0.000
0.006
7.104
0.000
0.001
7.048
0.000
0.002
Status
-------
changes as the state-level reference population.74 Therefore, whereas PM2.5 exposure impacts
vary considerably across states, the small magnitude of differential impacts expected by the
proposed rule is not likely to meaningfully exacerbate or mitigate EJ concerns within individual
states.
-jaN.<>:
Year Scenario Population
2028 Proposal
-------
6.5.2.3 Distributional Results
We also present cumulative proportion of each population exposed to ascending levels of
PM2.5 concentration changes across the contiguous U.S. Results allow evaluation of what
percentage of each subpopulation (e.g., Hispanics) in the contiguous U.S. experience what
change in PM2.5 concentrations compared to what percentage of the overall reference group (i.e.,
the total population of contiguous U.S.) experiences similar concentration changes from EGU
emission changes under the two regulatory options in 2028, 2030, and 2035 (Figure 6-4).
This distributional EJ analysis is also subject to additional uncertainties related to more
highly resolved input parameters and additional assumptions. For example, this analysis does not
account for potential difference in underlying susceptibility, vulnerability, or risk factors across
populations to PM2.5 exposure. Nor could we include information about differences in other
factors that could affect the likelihood of adverse impacts (e.g., exercise patterns) across groups.
Therefore, this analysis should not be used to assert that there are meaningful differences in
PM2.5 exposure impacts associated with either the baseline or the rule across population groups.
As the baseline scenario is similar to that described by other RIAs, we focus on the PM2.5
changes due to this proposed rulemaking. Distributions of 12 km2 gridded PM2.5 concentration
changes from EGU control strategies of affected facilities under the two regulatory options
analyzed in this proposed rulemaking in 2028, 2030, and 2035 are shown in Figure 6-4. For
clarity, only above/below the poverty line and those who speak English "well or better'V'less
than well" are shown and sex and the overall reference group are excluded from the cumulative
distribution figures.
The vast majority of PM2.5 concentration changes for each population distribution are less
than 0.02 |ig/m3 under either regulatory option for all three future years analyzed. Therefore, the
distributions of PM2.5 concentration changes across population demographics are all reasonably
similar and the very small difference in impacts shown in the distributional analyses of PM2.5
concentration changes under the various regulatory options provides additional evidence that the
proposed rule is not likely to meaningfully exacerbate or mitigate EJ PM2.5 exposure concerns
for population groups evaluated.
6-17
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2028
Group Policy Alternate
2030
Policy Alternate
2035
Policy Alternate
c 100%'
0
Race ° 3 50%-
^ Q.
0
a 0%.
¦ White (0-99)
American Indian (0-99) m
¦ Asian (0-99) M
¦ Black (0-99) ¥
_r
j
c 100%'
O
Ethnicity % ^ 50%-
CL
a¦ 0%.
1 \tr~~
—j
j
r
j
c 100%'
O
Educational o jo
Attainment & R
o
°¦ 0%.
¦ More educated
¦ Less educated i
J
j
r
j
c 100%'
o
Employment "S to
Status # I 50%'
o
a 0%.
¦ Employed (0-99) f
¦ Unemployed (0-99) yr
Not in the labor force (0-99) f
j
r
J
(
c 100%"
o
Insurance o ra
Status * | 50%'
a 0%.
¦ Insured (0-64)
¦ Unisured(0-64)
j
r
J
f
c 100%'
o
Linguistic t to
Isolation # q.
O
a 0%.
¦ English "well or better" (0-99) •
¦ English < "well" (0-99)
—
J
(
c 100%'
Poverty ||
Status ^ q.
o
"¦ 0%.
¦ >Poverty line (0-99) f
¦
-------
As this analysis is based on the same ozone spatial fields as the benefits assessment (see
Appendix A for a discussion of the spatial fields), it is subject to similar types of uncertainty (see
Section 4.3.8 for a discussion of the uncertainty). In addition to the small magnitude of
differential ozone concentration changes associated with this proposed rulemaking when
comparing across demographic populations, a particularly germane limitation is that ozone,
being a secondary pollutant, is the byproduct of complex atmospheric chemistry such that direct
linkages cannot be made between specific affected facilities and downwind ozone concentration
changes based on available air quality modeling.
Ozone concentration and exposure metrics can take many forms, although only a small
number are commonly used. The analysis presented here is based on the average April-
September warm season maximum daily eight-hour average ozone concentrations (AS-M03),
consistent with the health impact functions used in the benefits assessment (Section 4). As
developing spatial fields is time and resource intensive, the same spatial fields used for the
benefits analysis were also used for the ozone exposure analysis performed here to assess EJ
impacts.
The construct of the AS-M03 ozone metric used for this analysis should be kept in mind
when attempting to relate the results presented here to the ozone NAAQS and when interpreting
the confidence in the association between exposures and health effects. Specifically, the seasonal
average ozone metric used in this analysis is not constructed in a way that directly relates to
NAAQS design values, which are based on daily maximum eight-hour concentrations.75 Thus,
AS-M03 values reflecting seasonal average concentrations well below the level of the NAAQS
at a particular location do not necessarily indicate that the location does not experience any daily
(eight-hour) exceedances of the ozone NAAQS. Relatedly, EPA is confident that reducing the
highest ambient ozone concentrations will result in substantial improvements in public health,
including reducing the risk of ozone-associated mortality. However, the Agency is less certain
about the public health implications of changes in relatively low ambient ozone concentrations.
Most health studies rely on a metric such as the warm-season average ozone concentration; as a
result, EPA typically utilizes air quality inputs such as the AS-M03 spatial fields in the benefits
75 Level of 70 ppb with an annual fourth-highest daily maximum eight-hour concentration, averaged over three
years.
6-19
-------
assessment, and we judge them also to be the best available air quality inputs for this EJ ozone
exposure assessment.
6.5.3.1 National Aggregated Results
National average baseline ozone concentrations in ppb in 2028, 2030, and 2035 are
shown in the colored column labeled "baseline" in the heat map (Figure 6-5). Concentrations in
the "baseline" columns represent the total estimated daily eight-hour maximum ozone exposure
burden averaged over the 6-month April-September ozone season and are colored to visualize
differences more easily in average concentrations, with lighter green coloring representing
smaller average concentrations and darker green coloring representing larger average
concentrations. Populations with national average ozone concentrations higher than the reference
population ordered from most to least difference were: American Indians, Hispanics,
linguistically isolated, Asians, the less educated, and children. Average national disparities
observed in the baseline of this rule are fairly consistent across the three future years and similar
to those described by recent rules (e.g., the proposed GNP rule).
Columns labeled "Proposal" and "More Stringent" provide information regarding how
the proposed regulatory and more stringent options will impact ozone concentrations across
various populations, respectively.76 For all three future years evaluated, there were no discernable
ozone changes under the proposed regulatory option for any population analyzed when showing
concentrations out to the hundredths digit, reiterating the small magnitude of national average
ozone changes. Going to the hundredths digit did show small national-level ozone concentration
reductions for the more stringent regulatory option in all three future years, that were very
similar across all population groups evaluated.
The national-level assessment of ozone burden concentrations in the baseline and ozone
exposure changes due to the regulatory options suggests that while EJ exposure disparities are
present in the pre-policy scenario, meaningful EJ exposure concerns are not likely created or
76 We report average exposure results to the decimal place where difference between demographic populations
become visible, as we cannot provide a quantitative estimate of the air quality modeling precision uncertainty. Using
this approach allows for a qualitative consideration of uncertainties and the significance of the relatively small
differences.
6-20
-------
exacerbated by the rule for the population groups evaluated, due to the small magnitude of the
ozone concentration changes.
2028
More
Stringent
2030
More
Stringent
2035
More
Stringent
Group
Population
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Reference (0-99)
40.80
0.00
0.02
40.70
0.00
0.01
40.51
0.00
0.01
Race
White (0-99)
40.89
0.00
0.02
40.79
0.00
0.01
40.61
0.00
0.01
American Indian (0-99)
42.93
0.00
0.02
42.86
0.00
0.01
0.01
0.01
Asian (0-99)
41.98
0.00
0.01
41.85
0.00
0.01
0.00
0.01
Black (0-99)
39.54
0.00
0.02
39.42
0.00
0.01
39.17
0.01
0.01
Ethnicity
Non-Hispanic (0-99)
40.27
0.00
0.02
40.15
0.00
0.02
39.91
0.01
0.01
Hispanic (0-99)
42.75
0.00
0.02
42.64
0.00
0.01
42.44
0.00
0.01
Educational
Attainment
More educated (>24: HS or more)
Less educated (>24; no HS)
40.63
41.22
0.00
0.00
0.02
0.02
40.53
41.13
0.00
0.00
0.01
0.01
40.34
40.97
0.00
0.00
0.01
0.01
Employment
Status
Employed
Unemployed
Not in the labor force
41.23
40.80
40.77
0.00
0.00
0.00
0.02
0.02
0.02
BllM
40.70
40.67
0.00
0.00
0.00
0.01
0.01
0.01
40 95
40.51
40.48
0.00
0.00
0.00
0.01
0.01
0.01
Insurance
Status
Insured
Unisured
40.95
40.46
0.00
0.00
0.02
0.02
40.85
40.36
0.00
0.00
0.01
0.01
40.66
40.17
0.00
0.00
0.01
0.01
Linguistic
English "well or better" (0-99)
40.74
0.00
0.02
40.63
0.00
0.01
40.44
0.00
0.01
isolation
English < "well" (0-99)
42.15
0.00
0.01
42.06
0.00
0.01
41.90
0.00
0.01
Poverty
Status
>Povertyline (0-99)
-------
Outside of MT, South Dakota (SD), and Wyoming (WY), population averages within
individual states do not vary by more than 0.02 ppb. Elsewhere, populations potentially of
concern are projected to experience similar ozone concentration reductions as the state-level
reference population. Please note that population counts vary greatly by state and that as of 2022,
MT, SD, and WY were the 43rd, 46th, and 50th least populated states.77
Therefore, ozone exposure impacts vary considerably across states. In addition, although
American Indians in MT, SD, and WY may experience slightly greater reductions due to this
proposed rulemaking, the small magnitude of differential impacts expected by the proposed rule
is not likely to meaningfully exacerbate or mitigate EJ concerns within individual states.
77 Averaging results of the 48 states shown here will not reflect national population-weighted exposure estimates,
due to different populations within each state.
6-22
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Year Scenario
2028 Proposal
Ozone(ppb)
m
-0.02
0.10
More Stringent
2030 Proposal
More Stringent
2035 Proposal
More Stringent
Population
Reference
>
Figure 6-6 Heat Map of the State Average Ozone Concentrations Reductions (Green)
and Increases (Red) Due to the Proposed and More Stringent Regulatory Options Across
Demographic Groups in 2028, 2030, and 2035 (ppb)
6.5.3.3 Distributional Results
We also present cumulative proportion of each population exposed to ascending levels of
ozone concentration changes across the contiguous U.S. Results allow evaluation of what
percentage of each subpopulation (e.g., Hispanics) in the contiguous U.S. experience what
change in ozone concentrations compared to what percentage of the overall reference group (i.e.,
6-23
-------
the total population of contiguous U.S.) experiences similar concentration changes from EGU
emission changes under the two regulatory options in 2028, 2030, and 2035.
This distributional EJ analysis is also subject to additional uncertainties related to more
highly resolved input parameters and additional assumptions. For example, this analysis does not
account for potential difference in underlying susceptibility, vulnerability, or risk factors across
populations expected to experience post-policy ozone exposure changes. Nor could we include
information about differences in other factors that could affect the likelihood of adverse impacts
(e.g., exercise patterns) across groups. Therefore, this analysis should not be used to assert that
there are meaningful differences in ozone exposures impacts in either the baseline or the rule
across population groups.
As the baseline scenario is similar to that described by other RIAs, we focus on the ozone
changes due to this proposed rulemaking. Distributions of 12 km2 gridded ozone concentration
changes from EGU control strategies of affected facilities under the regulatory options analyzed
in this proposed rulemaking are shown in Figure 6-7. For clarity, only above/below the poverty
line and those who speak English "well or better'V'less than well" are shown and sex and the
overall reference group are excluded from the cumulative distribution figures.
The vast majority of ozone concentration changes are less than 0.05 ppb under either
regulatory option for all three future years analyzed. Therefore, the distributions of ozone
concentration changes across population demographics are all reasonably similar and the very
small difference shown in the distributional analyses of ozone concentration changes under the
two regulatory options provides additional evidence that the proposed rule is not likely to
meaningfully exacerbate or mitigate EJ ozone exposure concerns for population groups
evaluated.
6-24
-------
2028
Group Proposal More Stringent
2030
Proposal More Stringent
2035
Proposal More Stringent
c 100%"
o
Race J | 50%-
^ CL
o
0%.
J
H White (0-99)
American Indian (0-99)
¦ Asian (0-99)
¦ Black (0-99)
f~
r
f
J
I
C 100%"
_o
Ethnicity ° -= 50%-
CL
0%.
¦ Non-Hispanic (0-99)
¦ Hispanic (0-99)
r
r
f
j
I
c 100%"
o
Educational o <5
««. . 43 50%-
Attainment 8= g.
o
0%.
¦ More educated
¦ Less educated i
r
f
J
/ 1
c 100%"
o
Employment o Povertyline (0-99)
¦
-------
young, and pregnant or nursing women; those already in poor health or with comorbidities; the
disabled; those experiencing homelessness, mental illness, or substance abuse; and/or Indigenous
or minority populations dependent on one or limited resources for subsistence due to factors
including but not limited to geography, access, and mobility.
Scientific assessment reports produced over the past decade by the U.S. Global Change
Research Program (USGCRP),78'79 the rpcc,80'81'82'83 and the National Academies of Science,
Engineering, and Medicine84'85 add more evidence that the impacts of climate change raise
potential EJ concerns. These reports conclude that poorer or predominantly non-White
communities can be especially vulnerable to climate change impacts because they tend to have
78 USGCRP, 2018: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment,
Volume II [Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C.
Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 1515 pp. doi: 10.7930/NCA4.2018.
79 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment.
Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C.
Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim, J. Trtanj, andL. Ziska, Eds. U.S. Global Change
Research Program, Washington, DC, 312 pp. http://dx.doi.org/10.7930/J0R49NQX
80 Oppenheimer, M., M. Campos, R.Warren, J. Birkmann, G. Luber, B. O'Neill, and K. Takahashi, 2014: Emergent
risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatteijee,
K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and
L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039-
1099.
81 Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, andM.I. Travasso,
2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea,
T.E. Bilir, M. Chatteijee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,
P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, pp. 485-533.
82 Smith, K.R., A.Woodward, D. Campbell-Lendrum, D.D. Chadee, Y. Honda, Q. Liu, J.M. Olwoch, B. Revich, and
R. Sauerborn, 2014: Human health: impacts, adaptation, and co-benefits. In: Climate Change 2014: Impacts,
Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J.
Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatteijee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S.
Kissel,A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, pp. 709-754.
83IPCC, 2018: Global Wanning of 1.5°C.An IPCC Special Report on the impacts of global warming of 1.5°C above
pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global
response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-
Delmotte, V., P. Zhai, H.-O. Portner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Pean, R.
Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T.
Waterfield (eds.)]. In Press.
84 National Research Council. 2011. America's Climate Choices. Washington, DC: The National Academies Press.
https://doi.org/10.17226/12781.
85 National Academies of Sciences, Engineering, and Medicine. 2017. Communities in Action: Pathways to Health
Equity. Washington, DC: The National Academies Press, https://doi.org/10.17226/24624.
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limited adaptive capacities and are more dependent on climate-sensitive resources such as local
water and food supplies or have less access to social and information resources. Some
communities of color, specifically populations defined jointly by ethnic/racial characteristics and
geographic location, may be uniquely vulnerable to climate change health impacts in the U.S. In
particular, the 2016 scientific assessment on the Impacts of Climate Change on Human Health86
found with high confidence that vulnerabilities are place- and time-specific, lifestages and ages
are linked to immediate and future health impacts, and social determinants of health are linked to
greater extent and severity of climate change-related health impacts.
In a 2021 report, EPA considered the degree to which four socially vulnerable
populations—defined based on income, educational attainment, race and ethnicity, and age—
may be more exposed to the highest impacts of climate change.87 The report found that Blacks
and African American populations are approximately 40 percent more likely to live in areas of
the U.S. projected to experience the highest increases in mortality rates due to changes in
extreme temperatures. Additionally, Hispanic and Latino individuals in weather-exposed
industries were found to be 43 percent more likely to currently live in areas with the highest
projected labor hour losses due to extreme temperatures. American Indian and Alaska Native
individuals are projected to be 48 percent more likely to currently live in areas where the highest
percentage of land may be inundated by sea level rise. Overall, the report confirmed findings of
broader climate science assessments that Americans identifying as people of color, those with
low-income, and those without a high school diploma face disproportionate risks of experiencing
the most damaging impacts of climate change.
These findings suggest that CO2 reductions may benefit disproportionately impacted
populations. However, as we have not conducted the wide-ranging analyses that would be
needed to assess the specific impacts of this rule on the multiple climate-EJ interactions
described above, we cannot analyze the potential impacts of the proposed rule quantitatively.
86 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment
87 EPA 2021. Climate Change and Social Vulnerability in the United States: A Focus on Six Impacts. U.S.
Environmental Protection Agency, EPA 430-R-21-003.
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6.7 Summary
As with all EJ analyses, data limitations make it quite possible that disparities may exist
that our analysis did not identify. This is especially relevant for potential EJ characteristics,
environmental impacts, and more granular spatial resolutions that were not evaluated. For
example, here we provide qualitative EJ assessment of ozone and PM2.5 concentration changes
from this rule but can only qualitatively discuss EJ impacts of CO2 emission reductions.
Therefore, this analysis is only a partial representation of the distributions of potential impacts.
Additionally, EJ concerns for each rulemaking are unique and should be considered on a case-
by-case basis, so results similar to those presented here should not be assumed for other
rulemakings.
For the rule, we quantitatively evaluate the proximity of affected facilities populations of
potential EJ concern (Section 6.4) and the potential for disproportionate pre- and policy-policy
PM2.5 and ozone exposures across different demographic groups (Section 6.5). As exposure
results generated as part of the 2020 Residual Risk analysis were below both the presumptive
acceptable cancer risk threshold and the noncancer health benchmarks, and this proposed
regulation should still reduce exposure to HAP, there are no 'disproportionate and adverse
effects' of potential EJ concern. Therefore, we did not perform a quantitative EJ assessment of
HAP risk. Each of these analyses presented depend on mutually exclusive assumptions, was
performed to answer separate questions, and is associated with unique limitations and
uncertainties.
Baseline demographic proximity analyses provide information as to whether there may
be potential EJ concerns associated with local environmental stressors such as local NO2 and SO2
emitted from sources affected by the regulatory action, traffic, or noise for certain population
groups of concern in the baseline (Section 6.4). The baseline demographic proximity analyses
examined the demographics of populations living within 10 km of the following sources: lignite
plants with units potentially subject to the proposed mercury standard revision, coal plants with
units potentially subject to the proposed filterable PM standard revision, and coal plants with
units potentially subject to the alternate filterable PM standard revision. The proximity
demographic analysis indicates that on average the percentage of the population living within 10
km of coal plants potentially subject to the proposed or alternate filterable PM standards have a
6-28
-------
higher percentage of people living below two times the poverty level than the national average.
In addition, on average the percentage of the Native American population living within 10 km of
lignite plants potentially subject to proposed mercury standard is higher than the national
average. Relating these results to question 1 from Section 6.3, we conclude that there may be
potential EJ concerns associated with directly emitted pollutants that are affected by the
regulatory action (e.g., local NOx or SO2) for certain population groups of concern in the
baseline (question 1). However, as proximity to affected facilities does not capture variation in
baseline exposure across communities, nor does it indicate that any exposures or impacts will
occur, these results should not be interpreted as a direct measure of exposure or impact.
While the demographic proximity analyses may appear to parallel the baseline analysis of
nationwide ozone and PM2.5 exposures in certain ways, the two should not be directly compared.
The baseline ozone and PM2.5 exposure assessments are in effect an analysis of total burden in
the contiguous U.S., and include various assumptions, such as the implementation of
promulgated regulations. It serves as a starting point for both the estimated ozone and PM2.5
changes due to this proposal as well as a snapshot of air pollution concentrations in the near
future.
As HAP exposure results generated as part of the 2020 Residual Risk analysis were
below both the presumptive acceptable cancer risk threshold and the noncancer health
benchmarks, and this proposed regulation should further reduce exposure to HAP, there are no
'disproportionate and adverse effects' of potential EJ concern. Therefore, we did not perform a
quantitative EJ assessment of HAP risk.
This proposed rule is also expected to reduce emissions of direct PM2.5, NOx, and SO2
nationally throughout the year. Because NOx and SO2 are also precursors to secondary formation
of ambient PM2.5 and NOx is a precursor to ozone formation, reducing these emissions would
impact human exposure. Quantitative ozone and PM2.5 exposure analyses can provide insight
into all three EJ questions, so they are performed to evaluate potential disproportionate impacts
of this rulemaking.
The baseline ozone and PM2.5 exposure analyses respond to question 1 from EPA's EJ
Technical Guidance document more directly than the proximity analyses, as they evaluate a form
of the environmental stressor primarily affected by the regulatory action (Section 6.5). Baseline
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PM2.5 and ozone exposure analyses show that certain populations, such as Hispanic, Asian, those
linguistically isolated, those less educated, and children may experience disproportionately
higher ozone and PM2.5 exposures as compared to the national average. American Indian
populations may also experience disproportionately higher ozone concentrations than the
reference group. Therefore, there likely are potential EJ concerns associated with environmental
stressors affected by the regulatory action for population groups of concern in the baseline.
Finally, we evaluate how the post-policy options of this proposed rulemaking are
expected to differentially impact demographic populations, informing questions 2 and 3 from
EPA's EJ Technical Guidance with regard to ozone and PM2.5 exposure changes. Due to the
small magnitude of the exposure changes across population demographics associated with the
rulemaking relative to the magnitude of the baseline disparities, we infer that baseline disparities
in ozone and PM2.5 concentration burdens are likely to remain after implementation of the
regulatory or more stringent option under consideration (question 2). Also due to the very small
differences in the magnitude of post-policy ozone and PM2.5 exposure impacts across
demographic populations, we do not find evidence that potential EJ concerns related to ozone or
PM2.5 exposures will be meaningfully exacerbated or mitigated in the regulatory alternatives
under consideration, compared to the baseline (question 3). Importantly, the action described in
this rule is expected to lower ozone and PM2.5 in many areas, including those areas that struggle
to attain or maintain the NAAQS, and thus mitigate some pre-existing health risks across all
populations evaluated.
This EJ air quality analysis concludes that there are PM2.5 and ozone exposure disparities
across various populations in the pre-policy baseline scenario (EJ question 1) and infer that these
disparities are likely to persist after promulgation of this proposed rulemaking (EJ question 2).
This EJ assessment also suggests that this action will neither mitigate nor exacerbate PM2.5 and
ozone exposure disparities across populations of EJ concern analyzed (EJ question 3) at the
national scale in a meaningful way.
6-30
-------
7 COMPARISON OF BENEFITS AND COSTS
7.1 Introduction
This section presents the estimates of the health benefits, compliance costs, and net
benefits associated with the proposed MATS review relative to baseline MATS requirements.
All analysis begins in the year 2028, the compliance year for the proposed standards. In this RIA,
the regulatory impacts are evaluated for the specific years of 2028, 2030, and 2035. We also
evaluate the potential regulatory impacts of the regulatory options using the present value (PV)
and equivalent annualized value (EAV) of costs, benefits, and net benefits, calculated for the
years 2028 to 2037 from the perspective of 2023, using both a three percent and seven percent
end-of-period discount rate.
There are potential benefits and costs that may result from this proposed rule that have
not been quantified or monetized. Due to current data and modeling limitations, quantified and
monetized benefits from the proposed requirements from reducing mercury and non-mercury
metal HAP emissions are not included in the monetized benefits presented here.
The compliance costs reported in this RIA are not social costs, although in this analysis
we use compliance costs as a proxy for social costs. We do not account for changes in costs and
benefits due to changes in economic welfare of suppliers to the electricity market or to non-
electricity consumers from those suppliers. Furthermore, costs due to interactions with pre-
existing market distortions outside the electricity sector are omitted.
7.2 Methods
EPA calculated the PV of costs, benefits, and net benefits for the years 2028 through
2037, using both a three percent and seven percent end-of-period discount rate from the
perspective of 2023. All dollars are in 2019 dollars. In order to implement the OMB Circular A-4
requirement for fulfilling E.O. 12866, we assess one less stringent and one more stringent
alternative to the proposed requirements.
This calculation of a PV requires an annual stream of values for each year of the 2028 to
2037 timeframe. EPA used IPM to estimate cost and emission changes for the projection years
7-1
-------
2028, 2030, and 2035. The year 2028 is an approximation of the compliance year for the
proposed requirements. In the IPM modeling for this RIA, the 2028 projection year is
representative of 2028 alone, the 2030 projection year is representative of 2029 through 2031,
and the 2035 projection year is representative of 2032 to 2037.88 Estimates of costs and emission
changes in other years are determined from the mapping of projection years to the calendar years
that they represent. Consequently, the cost and emission estimates from IPM in each projection
year are applied to the years which it represents.89
Health benefits are based on projection year emission estimates and also account for
year-specific variables that influence the size and distribution of the benefits. These variables
include population growth, income growth, and the baseline rate of death.90 Climate benefits
estimates are based on these projection year emission estimates, and also account for year-
specific interim SC-CO2 values.91
EPA calculated the PV and EAV of costs, benefits, and net benefits over the 2028
through 2037 timeframe for the three regulatory options examined in this RIA. The EAV
represents a flow of constant annual values that, had they occurred in each year from 2028 to
2037, would yield an equivalent present value. The EAV represents the value of a typical cost or
benefit for each year of the analysis, in contrast to the year-specific estimates presented
elsewhere for the snapshot years of 2028, 2030, and 2035.
7.3 Results
We first present net benefit analysis for the three years of detailed analysis, 2028, 2030,
and 2035. Table 7-1, Table 7-2, and Table 7-3 present the estimates of the projected compliance
costs, health benefits, climate benefits, and net benefits across the regulatory options examined in
this proposal, respectively. The comparison of benefits and costs in PV and EAV terms for the
proposed rule can be found in Table 7-4 for the proposed regulatory option. Table 7-5 presents
88 For more information regarding the mapping of projection years to calendar years, see Documentation for EPA's
Power Sector Modeling Platform v6 Using the Integrated Planning Model (2022), available at:
https://www.epa.gov/airmarkets/clean-air-markets-power-sector-modeling
89 MR&R costs estimates are not based on IPM. For information on MR&R costs, see Section 3.
90 As these variables differ by year, the health benefit estimates vary by year, including when different years
are based on the same IPM projection year emission estimate.
91 As the interim SC-CO2 estimates vary by year, the climate benefit also estimates vary by year, even when
different years are based on the same IPM projection year emission estimate.
7-2
-------
the results for the less stringent regulatory option, and Table 7-6 presents results for the more
stringent regulatory option. Estimates in the tables are presented as rounded values. Note the less
stringent regulatory option has no quantified emissions reductions associated with the proposed
requirements for PM CEMS and the removal of startup definition number two. As a result, there
are no quantified benefits associated with this regulatory option.
Table 7-1 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less
and More Stringent Alternatives for 2028 for the U.S. (millions of 2019 dollars) a'b
Proposed Less Stringent More Stringent
Rule Alternative Alternative
PM2.5 and 03-related
Health Benefits c
58
and
140
0.0
and
0.0
1,300
and
3,100
Climate Benefits'1
13
0.0
1,300
Total Benefits®
71
and
160
0.0
and
0.0
2,600
and
4,400
Compliance Costs
56
-5.9
920
Net Benefits
16
and
100
5.9
and
5.9
1,700
and
3,500
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
d Climate benefits are based on reductions in CO2 emissions and are calculated using four different estimates of the
social cost of carbon dioxide (SC-CO2): model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th
percentile at 3 percent discount rate. The 95th percentile estimate is included to provide information on potentially
higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount
rate. For the presentational purposes of this table, we show the climate benefits associated with the average SC-CO2
at a 3 percent discount rate, but the Agency does not have a single central SC-CO2 point estimate. Climate benefits
in this table are discounted using a 3 percent discount rate to obtain the PV and EAV estimates in the table. We
emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. Section
4.4 of the RIA presents estimates of the projected climate benefits of this proposal using all four rates. We note that
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is
warranted when discounting intergenerational impacts.
e Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit
estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal
HAP emissions and from the increased transparency and accelerated identification of anomalous emission
anticipated from requiring CEMS.
7-3
-------
Table 7-2 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less
and More Stringent Alternatives for 2030 for the U.S. (millions of 2019 dollars) a'b
Proposed Less Stringent More Stringent
Rule Alternative Alternative
PM2.5 and 03-related
Health Benefits c
50
and
150
0.0
and
0.0
250
and
860
Climate Benefits'1
50
0
530
Total Benefits®
100
and
200
0.0
and
0.0
780
and
1,400
Compliance Costs
46
-5.9
1,100
Net Benefits
54
and
160
5.9
and
5.9
-270
and
340
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
d Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
e Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit
estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal
HAP emissions and from the increased transparency and accelerated identification of anomalous emission
anticipated from requiring CEMS.
Table 7-3 Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less
and More Stringent Alternatives for 2035 for the U.S. (millions of 2019 dollars) a,b
Proposed Less Stringent More Stringent
Rule Alternative Alternative
PM2.5 and 03-related
Health Benefits c
100
and
330
0.0
and
0.0
570
and
1,500
Climate Benefits'1
310
0.0
190
Total Benefits®
410
and
640
0.0
and
0.0
760
and
1,700
Compliance Costs
39
-5.9
280
Net Benefits
370
and
600
5.9
and
5.9
480
and
1,400
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
d Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
e Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit
estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal
HAP emissions and from the increased transparency and accelerated identification of anomalous emission
anticipated from requiring CEMS.
7-4
-------
Table 7-4 Proposed Rule: Present Values and Equivalent Annualized Values of
Projected Monetized Compliance Costs, Benefits, and Net Benefits for 2028 to 2037
(millions of 2019 dollars, discounted to 2023) a
PM2.5 and 03-related
Health Benefits
Climate
Benefits
Compliance
Net
Benefits
3%
7%
3%
vOStS
3%
7%
2028
140
130
13
56
100
87
2029
150
130
49
46
150
140
2030
150
140
50
46
160
140
2031
160
140
51
46
160
140
2032
310
270
290
39
560
530
2033
320
280
300
39
570
540
2034
320
290
300
39
590
550
2035
330
300
310
39
600
570
2036
340
310
310
39
620
580
2037
350
310
320
39
630
590
PM2.5 and 03-related
Health Benefits
Climate
Benefits
Compliance
Costs
Net
Benefits
Discount Rate
3%
7%
3%
3% 7%
3%
7%
Present
Value
1,90C
1 1,200
1,400
330 230
3,000
2,400
Equivalent
Annualized
Value
220
170
170
38 33
350
300
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b The health benefits estimates use the larger of the two benefits estimates presented in Table 7-1 . Monetized
benefits include those related to public health associated with reductions in PM2 5 and ozone concentrations. The
health benefits are associated with several point estimates.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits
include important benefits from reductions in mercury and non-mercury metal HAP.
7-5
-------
Table 7-5 Less Stringent Regulatory Option: Present Values and Equivalent
Annualized Values for the 2028 to 2037 Timeframe for Estimated Monetized Compliance
Costs, Benefits, and Net Benefits (millions of 2019 dollars, discounted to 2023) a
PM2.5 and 03-related
Health Benefits
Climate
Benefits
Compliance
Net
Benefits
3%
7%
3%
vOStS
3%
7%
2028
0.0
0.0
0.0
-5.9
5.9
5.9
2029
0.0
0.0
0.0
-5.9
5.9
5.9
2030
0.0
0.0
0.0
-5.9
5.9
5.9
2031
0.0
0.0
0.0
-5.9
5.9
5.9
2032
0.0
0.0
0.0
-5.9
5.9
5.9
2033
0.0
0.0
0.0
-5.9
5.9
5.9
2034
0.0
0.0
0.0
-5.9
5.9
5.9
2035
0.0
0.0
0.0
-5.9
5.9
5.9
2036
0.0
0.0
0.0
-5.9
5.9
5.9
2037
0.0
0.0
0.0
-5.9
5.9
5.9
PM25
and 03-related
Climate
Compliance
Net
Health Benefits
Benefits
Costs
Benefits
Discount Rate
3%
7%
3%
3% 7%
3%
7%
Present
Value
0.0
0.0
0.0
-45 -31
45
31
Equivalent
Annualized
0.0
0.0
0.0
-5.2 -4.5
5.2
4.5
Value
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b The health benefits estimates use the larger of the two benefits estimates presented in Table 7-. Monetized benefits
include those related to public health associated with reductions in PM2 5 and ozone concentrations. The health
benefits are associated with several point estimates.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits
include important benefits from reductions in mercury and non-mercury metal HAP.
7-6
-------
Table 7-6 More Stringent Regulatory Option: Present Values and Equivalent
Annualized Values for the 2028 to 2037 Timeframe for Estimated Monetized Compliance
Costs, Benefits, and Net Benefits (millions of 2019 dollars, discounted to 2023) a
PM2.5 and 03-related
Health Benefits
Climate
Benefits
Compliance
Net
Benefits
3%
7%
3%
vOSlS
3%
7%
2028
3,100
2,800
1,300
920
3,500
3,200
2029
840
750
520
1,100
300
210
2030
860
770
530
1,100
330
250
2031
890
800
540
1,100
370
280
2032
1,400
1,200
180
280
1,300
1,100
2033
1,400
1,300
190
280
1,300
1,200
2034
1,500
1,300
190
280
1,400
1,200
2035
1,500
1,400
190
280
1,400
1,300
2036
1,600
1,400
200
280
1,500
1,300
2037
1,600
1,400
200
280
1,500
1,300
PM2.5 and 03-related
Health Benefits
Climate
Benefits
Compliance
Costs
Net
Benefits
Discount Rate
3%
7%
3%
3% 7%
3%
7%
Present
Value
11,000
7,100
3,200
4,600 3,400
9,800
6,900
Equivalent
Annualized
Value
1,300
1,000
380
540 490
1,100
900
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b The health benefits estimates use the larger of the two benefits estimates presented in Table 7-. Monetized benefits
include those related to public health associated with reductions in PM2 5 and ozone concentrations. The health
benefits are associated with several point estimates.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits
include important benefits from reductions in mercury and non-mercury metal HAP.
The results presented in this section provide an incomplete overview of the effects of the
proposal, because important categories of benefits, including benefits from reducing mercury and
non-mercury metal HAP emissions, were not monetized and are therefore not directly reflected
in the quantified benefit-cost comparisons. We anticipate that taking non-monetized effects into
account would show the proposal to be more net beneficial than the tables in this section reflect.
7-7
-------
APPENDIX A: AIR QUALITY MODELING
A.l Introduction
As noted in Section 4, EPA used photochemical modeling to create air quality surfaces92
that were then used in air pollution health benefits calculations of the three regulatory control
alternatives of the proposed rule. The modeling-based surfaces captured air pollution impacts
resulting from changes in NOx, SO2, and direct PM2.5 emissions from EGUs. This appendix
describes the source apportionment modeling and associated methods used to create air quality
surfaces for the baseline scenario and two regulatory options (the proposed regulatory options
and the more stringent regulatory option) in three analytic years: 2028, 2030 and 2035. EPA
created air quality surfaces for the following pollutants and metrics: annual average PM2.5; April-
September average of 8-hr daily maximum (MDA8) ozone (AS-M03).
The ozone source apportionment modeling outputs are the same as those created for the
Regulatory Impact Analysis for the proposed Federal Implementation Plan Addressing Regional
Ozone Transport for the 2015 Ozone National Ambient Air Quality Standard (U.S. EPA, 2022c).
New PM source apportionment modeling outputs were created using the same inputs and
modeling configuration as were used for the available ozone source apportionment modeling.
The basic methodology for determining air quality changes is the same as that used in the RIAs
from multiple previous rules (U.S. EPA, 2019, 2020a, 2020b, 2021b, 2022c). EPA calculated
baseline and regulatory option EGU emissions estimates of NOx and SO2 for all three analysis
years using IPM (Section 3 of this RIA). EPA also used IPM outputs to estimate EGU emissions
of PM2.5 based on emission factors described in flat (U.S. EPA, 2021a). This appendix provides
additional details on the source apportionment modeling simulations and the associated analysis
used to create ozone and PM2.5 air quality surfaces.
A.2 Air Quality Modeling Simulations
The air quality modeling utilized a 2016-based modeling platform which included
meteorology and base year emissions from 2016 and projected future-year emissions for
'air quality surfaces" refers to continuous gridded spatial fields using a 12 km2 grid-cell resolution
A-l
-------
2026.93'94 The air quality modeling included photochemical model simulations for a 2016 base
year and 2026 future year to provide hourly concentrations of ozone and PM2.5 component
species nationwide. In addition, source apportionment modeling was performed for 2026 to
quantify the contributions to ozone from NOx emissions and to PM2.5 from NOx, SO2 and
directly emitted PM2.5 emissions from EGUs on a state-by-state basis. As described below, the
modeling results for 2016 and 2026, in conjunction with EGU emissions data for the baseline
and three regulatory options in 2028, 2030 and 2035 were used to construct the air quality
surfaces that reflect the influence of emissions changes between the baseline and two regulatory
options in each year.
The air quality model simulations (i.e., model runs) were performed using the
Comprehensive Air Quality Model with Extensions (CAMx) version 7.1095 (Ramboll Environ,
2021). The nationwide modeling domain (i.e., the geographic area included in the modeling)
covers all lower 48 states plus adjacent portions of Canada and Mexico using a horizontal grid
resolution of 12 km2 shown in Figure A-l. Model predictions of ozone and PM2.5 concentrations
were compared against ambient measurements (U.S. EPA, 2022a, 2022b). Ozone and PM2.5
model evaluations showed model performance that was adequate for applying these model
simulations for the purpose of creating air quality surfaces to estimate ozone and PM2.5 benefits.
93 Information on the emissions inventories used for the modeling described in U.S. EPA (2022d)
94 The air quality modeling performed to support the analyses in this proposed RIA can be found in U.S. EPA
(2022b).
95 This CAMx simulation set the Rscale NH3 dry deposition parameter to 0 which resulted in more realistic model
predictions of PM2 5 nitrate concentrations than using a default Rscale parameter of 1
A-2
-------
Figure A-l Air Quality Modeling Domain
The contributions to ozone and PM2.5 component species (e.g., sulfate, nitrate,
ammonium, elemental carbon (EC), organic aerosol (OA), and crustal material96) from EGU
emissions in individual states were modeled using the "source apportionment" tool approach. In
general, source apportionment modeling quantifies the air quality concentrations formed from
individual, user-defined groups of emissions sources or "tags." These source tags are tracked
through the transport, dispersion, chemical transformation, and deposition processes within the
model to obtain hourly gridded97 contributions from the emissions in each individual tag to
hourly gridded modeled concentrations. For this RIA we used the source apportionment
contribution data to provide a means to estimate of the effect of changes in emissions from each
group of emissions sources (i.e., each tag) to changes in ozone and PM2.5 concentrations.
Specifically, we applied outputs from source apportionment modeling for ozone and PM2.5
component species using the 2026 modeled case to obtain the contributions from EGUs
emissions in each state to ozone and PM2.5 component species concentrations in each 12 km2
model grid cell nationwide. Ozone contributions were modeled using the Anthropogenic
Precursor Culpability Assessment (APCA) tool and PM2.5 contributions were modeled using the
Particulate Matter Source Apportionment Technology (PSAT) tool (Ramboll Environ, 2021).
The ozone source apportionment modeling was performed for the period April through
96 Crustal material refers to elements that are commonly found in the earth's crust such as Aluminum, Calcium. Iron,
Magnesium. Manganese, Potassium, Silicon, Titanium and the associated oxygen atoms.
® Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from
each tag
A-3
-------
September to provide data for developing spatial fields for the April through September
maximum daily eight-hour (MDA8) (i.e., AS-M03) average ozone concentration exposure
metric. The PM2.5 source apportionment modeling was performed for a full-year to provide data
for developing annual average PM2.5 spatial fields. Table A-l provides state-level 2026 EGU
emissions that were tracked for each source apportionment tag.
Table A-l 2026 Emissions Allocated to Each Modeled State-EGU Source
Apportionment Tag
State Tag
Ozone Season NOx
Emissions (tons)
Annual NOx
emissions (tons)
Annual SO2
emissions (tons)
Annual PM2.5
emissions (tons)
AL
6,205
9,319
1,344
2,557
AR
5,594
9,258
22,306
1,075
AZ
1,341
3,416
2,420
814
CA
6,627
16,286
249
4,810
CO
5,881
12,725
7,311
1,556
CT
1,673
3,740
845
467
DC
37
39
0
53
DE
203
320
126
119
FL
11,590
22,451
8,784
6,555
GA
3,199
5,937
1,177
2,452
IA
8,008
17,946
9,042
1,182
ID
375
705
1
185
IL
8,244
16,777
31,322
3,018
IN
11,052
36,007
34,990
6,281
KS
3,166
4,351
854
709
KY
11,894
25,207
22,940
10,476
LA
10,895
16,949
11,273
3,119
MA
2,115
4,566
839
384
MD
1,484
3,008
273
783
ME
1,233
3,063
1,147
414
MI
11,689
22,378
31,387
3,216
MN
4,192
9,442
7,189
481
MO
10,075
34,935
105,916
3,617
MS
3,631
5,208
30
1,240
MT
3,908
8,760
3,527
1,426
NC
7,175
15,984
6,443
2,720
ND
8,053
19,276
26,188
1,265
NE
8,670
20,274
45,869
1,530
NH
224
483
159
93
NJ
1,969
4,032
915
729
NM
1,266
1,987
0
304
NV
1,577
3,017
0
901
NY
6,248
11,693
1,526
1,649
A-4
-------
Table A-l 2026 Emissions Allocated to Each Modeled State-EGU Source
Apportionment Tag
State Tag
Ozone Season NOx
Emissions (tons)
Annual NOx
emissions (tons)
Annual SO2
emissions (tons)
Annual PM2.5
emissions (tons)
OH
9,200
27,031
46,780
4,543
OK
2,412
3,426
2
828
OR
1,122
2,145
29
455
PA
12,386
23,965
9,685
3,785
RI
233
476
0
68
SC
3,251
7,134
6,292
2,082
SD
478
1,054
889
55
TL*
1,337
2,970
6,953
1,329
TN
790
2,100
1,231
845
TX
16,548
27,164
19,169
5,027
UT
3,571
10,915
11,040
693
VA
3,607
7,270
820
1,805
VT
2
4
0
4
WA
11,78
2,532
158
384
WI
2,097
4,304
821
1,084
WV
7,479
21,450
28,513
2,180
WY
5,026
11,036
8,725
629
* TL represents emissions occurring on tribal lands
Examples of the magnitude and spatial extent of ozone and PM2.5 contributions are
provided in Figure A-2 through Figure A-5 for EGUs in California, Texas, Iowa, and Ohio.
These figures show how the magnitude and the spatial patterns of contributions of EGU
emissions to ozone and PM2.5 component species depend on multiple factors including the
magnitude and location of emissions as well as the atmospheric conditions that influence the
formation and transport of these pollutants. For instance, NOx emissions are a precursor to both
ozone and PM2.5 nitrate. However, ozone and nitrate form under very different types of
atmospheric conditions, with ozone formation occurring in locations with ample sunlight and
ambient VOC concentrations while nitrate formation requires colder and drier conditions and the
presence of gas-phase ammonia. California's complex terrain that tends to trap air and allow
pollutant build-up combined with warm sunny summer and cooler dry winters and sources of
both ammonia and VOCs make its atmosphere conducive to formation of both ozone and nitrate.
While the magnitude of EGU NOx emissions in Iowa and California are similar in the 2026
modeling (Table A-l) the emissions from California lead to larger contributions to the formation
of those pollutants due to the conducive conditions in that state. Texas and Ohio both had larger
A-5
-------
NOx emissions than California or Iowa. While maximum ozone impacts shown for Texas and
Ohio EGUs are similar order of magnitude to maximum ozone impacts from California EGUs,
nitrate impacts are much smaller in Ohio and negligible in Texas due to less conducive
atmospheric conditions for nitrate formation in those locations. California EGU SO2 emissions in
the 2026 modeling are several orders of magnitude smaller than SO2 emissions in Ohio and
Texas (Table A-l) leading to much smaller sulfate contributions from California EGUs than
from Ohio and Texas EGUs. PM2.5 organic aerosol EGU contributions in this modeling come
from primary PM2.5 emissions rather than secondary atmospheric formation. Consequently, the
impacts of EGU emissions on this pollutant tend to occur closer to the EGU sources than impacts
of secondary pollutants (ozone, nitrate, and sulfate) which have spatial patterns showing a
broader regional impacts. These patterns demonstrate how the model is able to capture important
atmospheric processes which impact pollutant formation and transport from emissions sources.
a) Apr-Sep MDA8 03
b) Annual PM2 5 Nitrate
Ft—r— nJ
at
f
' ?\ _ J/
KJ T~1 r b*
11 \- - - 1 ~ v 4) }
..
7 { L 1
fruiJi
J)
HP
iSrAi\x4
-
r tj—^13—
vr
ft
%
4
Y L -y
CM
WQ fy
y \f m y \
:
\ ^
r
V
(
\
\
\
y
c) Annual PM2 5 Sulfate
d) Annual PM2 5 OA
/
Yf
iCMlMat'SMIat'lHU*)
Figure A-2 Maps of California EGU Tag Contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (jig/in3); c) Annual Average
PM2.5 Sulfate (jtig/ni3); d) Annual Average PM2.5 Organic Aerosol (jig/m3)
A-6
-------
a) Apr-Sep MDA8 03
'/
if
gr>
ft
p
[
Wr^YV
c) Annual PM2 5 Sulfate
W
\
b) Annual PM2 5 Nitrate
¦Li _ LS n *)
/
Ti L
•n:0,00&0 ot 11,11 Mm = 3 S*-3 nl (504,67)
d) Annual PM25 OA
/
—| (fjfc J \il
4AIQ/#
X-...
f
i* jH-" •;(
Figure A-3 Maps of Texas EGU Tag Contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate jug/ni3); c) Annual Average
PM2.5, Sulfate (fig/m3); d) Annual Average PM2.5 Organic Aerosol (jug/m3)
a) Apr-Sep MDA8 03
ti
c) Annual PM2 5 Sulfate
AA
b
i T\
I) K(vy, M4x • Ct5M0 at [20^.111
b) Annual PM2 5 Nitrate
rXV
J m
/ ''--Or \
d) Annual PM2 5 OA
V
m
\\
i»o.oow>k OU mw • 015* at
Figure A-4 Maps of Iowa EGU Tag contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (jig/in3); c) Annual Average
PM2.5 Sulfate (ng/m3); d) Annual Average PM2.5 Organic Aerosol (jig/m3)
A-7
-------
a) Apr-Sep MDA8 03 u m b) Annual PM2 5 Nitrate
Figure A-5 Maps of Ohio EGU Tag Contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (jig/in3); c) Annual Average
P1VI2.5 Sulfate (ug/1113); d) Annual Average PM2.5 Organic Aerosol (jig/m3)
A.3 Applying Modeling Outputs to Create Spatial Fields
In this section we describe the method for creating spatial fields of AS-M03 and annual
average PM2.5 based on the 2016 and 2026 modeling. The foundational data include (1) ozone
and speciated PM2.5 concentrations in each model grid cell from the 2016 and 2026 modeling, (2)
ozone and speciated PM2.5 contributions in 2026 of EGUs emissions from each state in each
model grid cell,98 (3) 2026 emissions from EGUs that were input to the contribution modeling
(Table A-1) and (4) the EGU emissions from IPM for baseline and the two regulatory options in
each analytic year. The method to create spatial fields applies scaling factors to gridded source
apportionment contributions based on emissions changes between 2026 projections and the
baseline and the two regulatory options to the 2026 contributions. This method is described in
detail below.
® Contributions from EGUs were modeled using projected emissions for 2026. The resulting contributions were
used to construct spatial fields in 2030, 2035 and 2040.
A-8
-------
Spatial fields of ozone and PM2.5 in 2026 were created based on "fusing" modeled data
with measured concentrations at air quality monitoring locations. To create the spatial fields for
each future emissions scenario these fused 2026 model fields are used in combination with 2026
state-EGU source apportionment modeling and the EGU emissions for each scenario and analytic
year. Contributions from each state-EGU contribution "tag" were scaled based on the ratio of
emissions in the year/scenario being evaluated to the emissions in the modeled 2026 scenario.
Contributions from tags representing sources other than EGUs are held constant at 2026 levels
for each of the scenarios and year. For each scenario and year analyzed, the scaled contributions
from all sources were summed together to create a gridded surface of total modeled ozone and
PM2.5. The process is described in a step-by-step manner below starting with the methodology
for creating AS-M03 spatial fields followed by a description of the steps for creating annual
PM2.5 spatial fields.
Ozone:
1. Create fused spatial fields of 2026 AS-M03 incorporating information from the air quality
modeling and from ambient measured monitoring data. The enhanced Voronoi Neighbor
Average (eVNA) technique (Ding et al., 2016; Gold et al., 1997; U.S. EPA, 2007)was
applied to ozone model predictions in conjunction with measured data to create
modeled/measured fused surfaces that leverage measured concentrations at air quality
monitor locations and model predictions at locations with no monitoring data.
1.1. The AS-M03 eVNA spatial fields are created for the 2016 base year with EPA's
software package, Software for the Modeled Attainment Test - Community Edition
(SMAT-CE)99 (U.S. EPA, 2022f) using three years of monitoring data (2015-2017) and
the 2016 modeled data.
1.2. The model-predicted spatial fields (i.e., not the eVNA fields) of AS-M03 in 2016 were
paired with the corresponding model-predicted spatial fields in 2026 to calculate the
ratio of AS-M03 between 2016 and 2026 in each model grid cell.
99 SMAT-CE available for download at https://www.epa.gov/scram/photochemical-modeling-tools.
A-9
-------
1.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in
step 1.2 were multiplied by the corresponding eVNA spatial fields for 2016 created in
step 1.1 to produce an eVNA AS-M03 spatial field for 2026 using (Eq-1).
f t tat a \ Modelg,2026 Eq-1
eVNAg,2026 - (eVNAg2016) x Model&2oi(.
• eVNAg 2026 is the eVNA concentration of AS-M03 or PM2.5 component species in grid-
cell, g, in the 2026 future year
• eVNAg 2016 is the eVNA concentration of AS-M03 or PM2.5 component species in grid-
cell, g, in 2016
• Modelg 2026 is the CAMx modeled concentration of AS-M03 or PM2.5 component
species in grid-cell, g, in the 2026 future year
• Modelg 2016 is the CAMx modeled concentration of AS-M03 or PM2.5 component in
grid-cell, g, in 2016
2. Create gridded spatial fields of total EGU AS-M03 contributions for each combination of
scenario and analytic year evaluated.
2.1. Use the EGU ozone season NOx emissions for the 2028 baseline and the corresponding
2026 modeled EGU ozone season emissions (Table A-l) to calculate the ratio of 2028
baseline emissions to 2026 modeled emissions for each EGU state contribution tag (i.e.,
an ozone scaling factor calculated for each state).100 These scaling factors are provided in
Table A-2.
2.2. Calculate adjusted gridded AS-M03 EGU contributions that reflect differences in state-
EGU NOx emissions between 2026 and the 2028 baseline by multiplying the ozone
i°° preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were
tagged especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied to
any tags that tracked less than 100 tpy emissions in the original source apportionment modeling. Any emissions
changes in the low emissions state were assigned to a nearby state as denoted in Table A-2 through Table A-5.
A-10
-------
season NOx scaling factors by the corresponding gridded AS-M03 ozone
contributions101 from each state-EGU tag.
2.3. Add together the adjusted AS-M03 contributions for each EGU-state tag to produce
spatial fields of adjusted EGU totals for the 2028 baseline.102
2.4. Repeat steps 2.1 through 2.3 for the two 2028 regulatory options and for the baseline and
regulatory options for each additional analytic year. All scaling factors for the baseline
scenario and the regulatory control alternatives are provided in Table A-2.
3. Create a gridded spatial field of AS-M03 associated with IPM emissions for the 2028
baseline by combining the EGU AS-M03 contributions from step 2.3 with the corresponding
contributions to AS-M03 from all other sources. Repeat for each of the EGU contributions
created in step 2.4 to create separate gridded spatial fields for the baseline and two regulatory
options for the two other analytic years.
Steps 2 and 3 in combination can be represented by equation 2:
• AS-M03g i y is the estimated fused model-obs AS-M03 for grid-cell, "g," scenario, "i,"103 and
• eVNAg 2026 is the 2026 eVNA future year AS-M03 concentration for grid-cell "g" calculated
using Eq-1.
• Cg,Tot is the total modeled AS-M03 for grid-cell "g" from all sources in the 2026 source
apportionment modeling
101 The source apportionment modeling provided separate ozone contributions for ozone formed in VOC-limited
chemical regimes (03 V) and ozone formed in NOx-limited chemical regimes (03N). The emissions scaling factors
are multiplied by the corresponding 03N gridded contributions to MD A8 concentrations. Since there are no
predicted changes in VOC emissions in the control scenarios, the 03 V contributions remain unchanged.
102 The contributions from the unaltered 03 V tags are added to the summed adjusted 03N EGU tags.
103 Scenario "i" can represent either the baseline or one of the two regulatory options
104 Year "y" can represent 2028, 2030 or 2035
AS-M03g^y — eVNAg2026
Eq-2
year, "y;"104
A-ll
-------
• Cg,bc is the 2026 AS-M03 modeled contribution from the modeled boundary inflow;
• Cg,int is the 2026 AS-M03 modeled contribution from international emissions within the
modeling domain;
• Cg bio is the 2026 AS-M03 modeled contribute/on from biogenic emissions;
• Cg,fires is the 2026 AS-M03 modeled contribution from fires;
• Cg,USanthro is the total 2026 AS-M03 modeled contribution from U.S. anthropogenic sources
other than EGUs;
• CEGUV0C,g,t is the 2026 AS-M03 modeled contribution from EGU emissions of VOCs from state,
"t";
• CEGUNOx&t is the 2026 AS-M03 modeled contribution from EGU emissions of NOx from state,
"t"; and
• SNOx t J y is the EGU NOx scaling factor for state, "t," scenario, "i," and year, "y."
PM2.5
4. Create fused spatial fields of 2026 annual PM2.5 component species incorporating
information from the air quality modeling and from ambient measured monitoring data. The
eVNA technique was applied to PM2.5 component species model predictions in conjunction
with measured data to create modeled/measured fused surfaces that leverage measured
concentrations at air quality monitor locations and model predictions at locations with no
monitoring data.
4.1. The quarterly average PM2.5 component species eVNA spatial fields are created for the
2016 base year with EPA's SMAT-CE software package using three years of monitoring
data (2015-2017) and the 2016 modeled data.
4.2. The model-predicted spatial fields (i.e., not the eVNA fields) of quarterly average PM2.5
component species in 2016 were paired with the corresponding model-predicted spatial
fields in 2026 to calculate the ratio of PM2.5 component species between 2016 and 2026
in each model grid cell.
4.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in
step 4.2 were multiplied by the corresponding eVNA spatial fields for 2016 created in
A-12
-------
step 4.1 to produce an eVNA annual average PM2.5 component species spatial field for
2026 using Eq-1.
5. Create gridded spatial fields of total EGU speciated PM2.5 contributions for each combination
of scenario and analytic year evaluated.
5.1. Use the EGU annual total NOx, SO2 and PM2.5 emissions for the 2028 baseline scenario
and the corresponding 2026 modeled EGU NOx, SO2 and PM2.5 emissions from Table
A-l to calculate the ratio of 2028 baseline emissions to 2026 modeled emissions for each
EGU state contribution tag (i.e., annual nitrate, sulfate and directly emitted PM2.5 scaling
factors calculated for each state).105 These scaling factors are provided in Table A-3
through Table A-5.
5.2. Calculate adjusted gridded annual PM2.5 component species EGU contributions that
reflect differences in state-EGUNOx, SO2 and primary PM2.5 emissions between 2026
and the 2028 baseline by multiplying the annual nitrate, sulfate and directly emitted
PM2.5 scaling factors by the corresponding annual gridded PM2.5 component species
contributions from each state-EGU tag.106
5.3. Add together the adjusted PM2.5 contributions of for each EGU state tag to produce
spatial fields of adjusted EGU totals for each PM2.5 component species.
5.4. Repeat steps 5.1 through 5.3 for the two regulatory options in 2028 and for the baseline
and regulatory options for each additional analytic year. The scaling factors for all PM2.5
component species for the baseline and regulatory control alternatives are provided in
Table A-3 through Table A-5.
6. Create gridded spatial fields of each PM2.5 component species for the 2028 baseline by
combining the EGU annual PM2.5 component species contributions from step 5.3 with the
105 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were
tagged especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied to
any tags that had less than 100 tpy emissions in the original source apportionment modeling. Any emissions changes
in the low emissions state were assigned to a nearby state as denoted in Table A-2 through Table A-5.
106 Scaling factors for components that are formed through chemical reactions in the atmosphere were created as
follows: scaling factors for sulfate were based on relative changes in annual SO2 emissions; scaling factors for
nitrate were based on relative changes in annual NOx emissions. Scaling factors for PM2 5 components that are
emitted directly from the source (OA, EC, crustal) were based on the relative changes in annual primary PM2 5
emissions between the 2026 modeled emissions and the baseline and the three regulatory control alternatives in each
year.
A-13
-------
corresponding contributions to annual PM2.5 component species from all other sources.
Repeat for each of the EGU contributions created in step 5.4 to create separate gridded
spatial fields for the baseline and three regulatory control alternatives for all other analytic
years.
7. Create gridded spatial fields of total PM2.5 mass by combining the component species
surfaces for sulfate, nitrate, organic aerosol, elemental carbon, and crustal material with
ammonium, and particle-bound. Ammonium and particle-bound water concentrations are
calculated for each scenario based on nitrate and sulfate concentrations along with the
ammonium degree of neutralization in the base year modeling (2016) in accordance with
equations from the SMAT-CE modeling software (U.S. EPA, 2022f).
Steps 5 and 6 result in Eq-3 for PM2.5 component species: sulfate, nitrate, organic aerosol,
elemental carbon, and crustal material.
• PMS g i y is the estimated fused model-obs PM component species "s" for grid-cell, "g," scenario,
• eVNAs &2026 is the 2026 eVNA PM concentration for component species "s" in grid-cell "g"
calculated using Eq-1.
• Cs,g,Tot is the total modeled PM component species "s" for grid-cell "g" from all sources in the
2026 source apportionment modeling
• CS,&bc is the 2026 PM component species "s" modeled contribution from the modeled boundary
inflow;
• CSj&mt is the 2026 PM component species "s" modeled contribution from international emissions
within the modeling domain;
107 Scenario "i" can represent either baseline or one of the regulatory options.
108 Year "y" can represent 2028, 2030 or 2035.
PMs,g,i,y — 6VNAs^2026
Eq-3
"i,"107 and year, "y;"108
A-14
-------
• Cs,g,bio is the 2026 PM component species "s" modeled contribution from biogenic emissions;
• Cs,g,fires is the 2026 PM component species "s" modeled contribution from fires;
• Cs,g,usanthro is the total 2026 PM component species "s" modeled contribution from U.S.
anthropogenic sources other than EGUs;
• CEGUs g t is the 2026 PM component species "s" modeled contribution from EGU emissions of
NOx, SO2, or primary PM2.5 from state, "t"; and
• Ss ti y is the EGU scaling factor for component species "s," state "t," scenario "i," and year "y."
Scaling factors for nitrate are based on annual NOx emissions, scaling factors for sulfate are based
on annual SO2 emissions, scaling factors for primary PM2 5 components are based on primary
PM2 5 emissions
A.4 Scaling Factors Applied to Source Apportionment Tags
Table A-2 Ozone Scaling Factors for EGU Tags in the Baseline, the Proposed Rule, and
More Stringent Alternative
„ _ , _ , , _ More Stringent Regulatory
Baseline Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
AL
0.85
0.85
0.85
0.89
0.89
0.89
0.58
0.59
0.58
AR
0.38
0.38
0.33
0.27
0.27
0.28
0.20
0.20
0.20
AZ
1.28
1.29
1.27
2.05
2.04
2.05
2.80
2.81
2.82
CA
0.69
0.69
0.69
0.37
0.37
0.37
0.27
0.28
0.28
CO
0.71
0.72
0.61
0.16
0.16
0.16
0.16
0.16
0.16
CT
0.71
0.71
0.72
0.70
0.70
0.70
0.66
0.66
0.66
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.68
1.68
1.68
1.68
1.68
1.68
0.96
0.95
0.95
FL
1.09
1.09
1.03
1.02
1.02
1.01
0.91
0.91
0.88
GA
1.23
1.23
1.29
1.32
1.32
1.36
0.70
0.70
0.70
IA
1.28
1.28
1.28
0.96
0.96
0.96
0.05
0.04
0.04
ID
1.06
1.09
1.10
1.16
1.19
1.19
0.37
0.43
0.43
IL
0.42
0.41
0.42
0.40
0.40
0.40
0.27
0.27
0.27
IN
0.75
0.74
0.70
0.55
0.55
0.51
0.22
0.22
0.22
KS
1.02
1.02
1.03
0.16
0.16
0.16
0.06
0.06
0.06
KY
0.36
0.36
0.36
0.40
0.39
0.32
0.20
0.20
0.14
LA
0.47
0.47
0.46
0.46
0.46
0.46
0.32
0.32
0.32
MA
1.20
1.20
1.22
1.21
1.21
1.21
1.17
1.17
1.17
MD
0.74
0.74
0.83
0.74
0.74
0.73
0.70
0.69
0.70
ME
1.63
1.63
1.63
1.14
1.14
1.14
1.07
1.07
1.07
MI
0.73
0.73
0.60
0.74
0.73
0.70
0.57
0.57
0.56
MN
0.67
0.67
0.67
0.31
0.31
0.31
0.14
0.14
0.13
MO
0.53
0.53
0.54
0.25
0.25
0.27
0.04
0.03
0.03
A-
15
-------
Baseline
t. , r, „ More Stringent Regulatory
Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
MS
0.73
0.73
0.73
0.73
0.73
0.73
0.62
0.57
0.57
MT
1.01
1.01
1.01
0.97
0.97
0.97
0.93
0.01
0.12
NC
0.56
0.56
0.57
0.36
0.36
0.36
0.33
0.34
0.34
ND
1.46
1.46
1.20
1.07
1.07
0.87
0.50
0.50
0.50
NE
1.15
1.15
1.12
0.91
0.91
0.88
0.13
0.14
0.11
NH
1.25
1.24
1.33
1.30
1.30
1.30
1.04
1.04
1.04
NJ
1.06
1.06
1.03
1.07
1.07
1.07
0.96
0.96
0.95
NM
0.58
0.58
0.58
0.58
0.60
0.61
0.46
0.46
0.46
NV
0.74
0.75
0.68
1.12
1.13
1.10
0.98
1.04
1.04
NY
0.89
0.89
0.90
0.85
0.85
0.85
0.64
0.64
0.64
OH
0.78
0.78
0.79
0.59
0.60
0.53
0.32
0.33
0.33
OK
0.74
0.74
0.69
0.67
0.67
0.62
0.12
0.12
0.12
OR
0.33
0.33
0.34
0.10
0.10
0.10
0.00
0.00
0.00
PA
0.65
0.65
0.61
0.74
0.75
0.73
0.57
0.58
0.58
RI
1.26
1.26
1.27
1.26
1.26
1.26
1.13
1.13
1.13
SC
0.98
0.98
0.98
0.61
0.61
0.60
0.43
0.43
0.43
SD
1.33
1.33
1.37
1.06
1.06
1.17
0.08
0.08
0.08
TL
1.08
1.08
1.08
1.03
1.02
1.00
0.00
0.00
0.00
TN
1.99
1.99
2.00
0.92
0.92
0.82
0.57
0.57
0.57
TX
0.73
0.73
0.73
0.64
0.64
0.64
0.44
0.44
0.44
UT
1.02
1.02
1.01
1.10
1.10
1.10
0.97
1.08
1.09
VA
1.22
1.21
1.20
1.00
1.00
1.00
0.89
0.88
0.84
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.71
0.71
0.71
0.79
0.78
0.78
0.49
0.49
0.49
WI
1.29
1.29
1.29
0.96
0.96
0.96
0.51
0.51
0.51
WV
1.03
1.01
1.04
0.82
0.77
0.85
0.28
0.27
0.28
WY
0.70
0.70
0.49
0.61
0.61
0.38
0.62
0.62
0.38
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tons per year (tpy) emissions assigned in the
original source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
NOx, the following emissions change assignments were applied: DC -> MD, VT -> NY.
A-16
-------
Table A-3 Nitrate Scaling Factors for EGU Tags in the Baseline, the Proposed Rule,
and More Stringent Alternative
„ _ , _ , , _ More Stringent Regulatory
Baseline Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
AL
1.08
1.07
1.07
1.13
1.13
1.13
0.63
0.63
0.62
AR
0.43
0.43
0.44
0.34
0.34
0.34
0.17
0.17
0.17
AZ
1.36
1.36
1.30
1.66
1.66
1.66
1.80
1.81
1.81
CA
0.59
0.59
0.59
0.42
0.42
0.42
0.30
0.30
0.30
CO
0.57
0.57
0.52
0.16
0.16
0.16
0.18
0.18
0.18
CT
0.68
0.68
0.68
0.65
0.65
0.64
0.58
0.58
0.58
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.66
1.65
1.66
1.66
1.65
1.66
0.94
0.93
0.94
FL
1.15
1.15
1.06
1.04
1.04
1.04
0.98
0.98
0.96
GA
1.30
1.30
1.51
1.28
1.28
1.30
0.72
0.72
0.72
IA
1.28
1.28
1.31
0.98
0.98
0.99
0.04
0.04
0.04
ID
0.98
1.02
1.03
1.07
1.10
1.11
0.66
0.70
0.71
IL
0.41
0.41
0.41
0.40
0.40
0.40
0.21
0.20
0.21
IN
0.77
0.77
0.69
0.57
0.57
0.51
0.15
0.15
0.15
KS
1.73
1.73
1.63
0.20
0.20
0.19
0.09
0.09
0.09
KY
0.47
0.47
0.45
0.41
0.41
0.32
0.25
0.25
0.17
LA
0.62
0.62
0.63
0.60
0.60
0.60
0.35
0.35
0.35
MA
1.22
1.22
1.24
1.22
1.22
1.22
1.18
1.18
1.18
MD
0.84
0.84
0.90
0.81
0.81
0.81
0.72
0.71
0.72
ME
1.49
1.49
1.49
1.08
1.08
1.08
0.93
0.93
0.93
MI
0.70
0.70
0.57
0.73
0.73
0.68
0.47
0.47
0.47
MN
0.62
0.62
0.63
0.27
0.27
0.27
0.13
0.13
0.12
MO
0.83
0.83
0.83
0.56
0.56
0.63
0.05
0.05
0.05
MS
0.88
0.88
0.87
0.99
0.99
0.99
0.66
0.63
0.62
MT
1.05
1.05
1.05
1.01
1.01
1.01
1.06
0.64
0.69
NC
0.75
0.75
0.69
0.32
0.32
0.32
0.30
0.30
0.30
ND
1.48
1.47
1.20
1.01
1.01
0.90
0.52
0.52
0.52
NE
1.11
1.11
1.08
0.88
0.88
0.85
0.14
0.15
0.14
NH
1.11
1.11
1.16
1.13
1.13
1.13
1.00
1.00
1.00
NJ
1.06
1.06
1.05
1.08
1.08
1.08
0.87
0.87
0.87
NM
0.56
0.56
0.56
0.57
0.59
0.59
0.48
0.48
0.48
NV
0.58
0.58
0.57
0.88
0.88
0.89
0.76
0.78
0.79
NY
0.94
0.93
0.95
0.92
0.92
0.92
0.70
0.70
0.70
OH
0.83
0.83
0.80
0.57
0.57
0.51
0.30
0.31
0.29
OK
0.85
0.85
0.81
0.80
0.79
0.76
0.18
0.18
0.17
OR
0.54
0.54
0.56
0.24
0.24
0.24
0.12
0.12
0.12
PA
0.65
0.65
0.63
0.75
0.75
0.74
0.54
0.54
0.54
RI
1.19
1.19
1.22
1.19
1.19
1.19
1.07
1.07
1.07
SC
1.01
1.01
1.01
0.63
0.63
0.62
0.49
0.49
0.49
A-17
-------
„ _ , _ , , _ More Stringent Regulatory
Baseline Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
SD
1.28
1.28
1.30
1.01
1.01
1.06
0.04
0.04
0.04
TL
0.93
0.93
0.93
0.93
0.93
0.93
0.00
0.00
0.00
TN
1.58
1.57
1.57
0.69
0.69
0.66
0.48
0.47
0.47
TX
0.97
0.97
0.98
0.85
0.85
0.85
0.54
0.54
0.54
UT
0.56
0.56
0.56
0.60
0.59
0.60
0.56
0.59
0.60
VA
1.29
1.27
1.27
1.08
1.08
1.08
0.89
0.89
0.87
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.72
0.72
0.73
0.97
0.97
0.98
0.94
0.88
0.88
WI
1.46
1.46
1.48
1.02
1.02
1.02
0.45
0.45
0.45
WV
1.08
1.07
1.04
0.70
0.68
0.75
0.30
0.30
0.31
WY
0.68
0.68
0.50
0.59
0.59
0.37
0.61
0.61
0.38
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source
apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For NOx, the
following emissions change assignments were applied: DC -> MD, VT -> NY.
A-18
-------
Table A-4 Sulfate Scaling Factors for EGU Tags in the Baseline, the Proposed Rule,
and More Stringent Alternative
,, _ , „ , , _ More Stringent Regulatory
Baseline Proposed Regulatory Option ® "
State
Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
AL
1.88
1.88
1.88
1.79
1.79
1.83
0.61
0.62
0.64
AR
0.06
0.06
0.08
0.01
0.01
0.01
0.00
0.00
0.00
AZ
1.02
0.91
1.18
1.86
1.86
1.86
3.55
3.55
3.55
CA
2.42
2.42
2.42
0.43
0.43
0.43
0.40
0.40
0.40
CO
0.16
0.16
0.17
0.04
0.04
0.04
0.00
0.00
0.00
CT
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
FL
1.50
1.50
0.64
0.99
0.99
0.83
0.81
0.81
0.77
GA
3.61
3.61
4.84
2.75
2.75
3.14
0.00
0.00
0.00
IA
1.23
1.23
1.25
0.95
0.95
0.96
0.04
0.04
0.04
ID
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
IL
0.29
0.29
0.29
0.22
0.22
0.22
0.09
0.09
0.09
IN
1.18
1.17
1.14
0.64
0.64
0.62
0.16
0.16
0.16
KS
3.03
3.03
2.92
0.00
0.00
0.00
0.00
0.00
0.00
KY
0.31
0.31
0.27
0.31
0.31
0.18
0.17
0.18
0.08
LA
0.18
0.18
0.18
0.03
0.03
0.03
0.03
0.03
0.03
MA
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
MD
2.62
2.62
3.95
1.99
1.99
1.99
0.99
0.99
0.99
ME
1.11
1.11
1.11
0.88
0.88
0.88
0.81
0.81
0.81
MI
0.24
0.24
0.20
0.41
0.41
0.40
0.40
0.40
0.40
MN
0.61
0.61
0.61
0.47
0.47
0.47
0.13
0.13
0.13
MO
0.43
0.43
0.43
0.31
0.31
0.43
0.03
0.03
0.04
MS
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
MT
1.36
1.36
1.36
1.15
1.15
1.15
1.10
0.69
0.74
NC
0.65
0.65
0.61
0.10
0.10
0.12
0.05
0.05
0.05
ND
1.10
1.09
1.14
0.95
0.95
1.02
0.71
0.71
0.71
NE
1.05
1.05
1.01
0.97
0.97
0.93
0.17
0.17
0.16
NH
0.52
0.52
0.52
0.52
0.52
0.52
0.52
0.52
0.52
NJ
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NM
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NV
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NY
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
OH
0.70
0.70
0.65
0.45
0.45
0.30
0.07
0.07
0.06
OK
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
OR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
PA
0.78
0.78
0.80
0.58
0.56
0.60
0.30
0.27
0.30
RI
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.44
1.43
1.44
0.55
0.55
0.55
0.24
0.24
0.24
A-19
-------
State
Tag
Baseline
Proposed Regulatory Option
More Stringent Regulatory
Option
2028
2030
2035
2028
2030
2035
2028
2030
2035
SD
1.33
1.33
1.33
1.00
1.00
1.05
0.00
0.00
0.00
TL
0.98
0.98
0.98
0.98
0.98
0.98
0.00
0.00
0.00
TN
2.33
2.32
2.34
0.19
0.19
0.13
0.00
0.00
0.00
TX
1.48
1.47
1.74
0.66
0.66
0.67
0.72
0.72
0.72
UT
0.89
0.89
0.89
1.03
1.03
1.03
1.03
1.03
1.03
VA
1.13
1.13
1.13
1.13
1.13
1.12
0.93
0.93
0.93
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.34
0.34
0.34
0.21
0.21
0.21
0.21
0.21
0.21
WI
2.83
2.83
2.94
1.00
0.99
1.00
0.00
0.00
0.00
WV
1.15
1.15
1.06
0.58
0.57
0.66
0.17
0.17
0.16
WY
1.30
1.30
0.99
0.99
0.98
0.53
1.07
1.07
0.53
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source
apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For SO2, the
following emissions change assignments were applied: DC -> MD, ID -> MT, MS -> AL, NV -> UT, NM -> AZ,
OK TX, OR WA, RI CT, VT ^ NY.
A-20
-------
Table A-5 Primary PM2.5 Scaling Factors for EGU Tags in the Baseline, the Proposed
Rule, and More Stringent Alternative
„ _ , _ , , _ More Stringent Regulatory
Baseline Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
AL
1.06
1.06
1.05
1.08
1.08
1.08
0.80
0.80
0.80
AR
0.85
0.85
0.89
0.73
0.73
0.74
0.39
0.39
0.40
AZ
1.14
1.14
1.02
1.59
1.59
1.59
1.45
1.45
1.46
CA
0.68
0.68
0.68
0.54
0.54
0.54
0.40
0.40
0.40
CO
0.63
0.63
0.58
0.34
0.34
0.34
0.35
0.35
0.35
CT
0.59
0.59
0.61
0.53
0.53
0.53
0.39
0.39
0.39
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.35
1.34
1.35
1.36
1.34
1.34
0.96
0.96
0.96
FL
0.98
0.98
0.91
0.93
0.93
0.92
0.89
0.89
0.88
GA
0.86
0.86
0.88
0.91
0.91
0.90
0.76
0.76
0.76
IA
1.45
1.42
1.40
1.20
1.17
1.16
0.18
0.18
0.18
ID
0.99
1.05
1.07
1.15
1.20
1.23
0.78
0.85
0.86
IL
0.41
0.41
0.41
0.42
0.42
0.42
0.25
0.25
0.25
IN
0.77
0.77
0.71
0.61
0.61
0.57
0.32
0.32
0.32
KS
1.06
1.06
0.94
0.12
0.12
0.12
0.05
0.05
0.05
KY
0.14
0.14
0.14
0.13
0.13
0.12
0.09
0.09
0.09
LA
0.87
0.87
0.87
0.87
0.87
0.86
0.68
0.68
0.68
MA
0.99
0.99
1.01
0.99
0.99
0.99
0.85
0.85
0.85
MD
0.67
0.67
0.73
0.65
0.65
0.65
0.51
0.50
0.50
ME
1.08
1.08
1.09
1.03
1.03
1.03
0.98
0.98
0.98
MI
0.58
0.58
0.60
0.65
0.65
0.66
0.49
0.49
0.51
MN
1.02
1.02
1.02
0.44
0.44
0.43
0.26
0.26
0.26
MO
0.46
0.46
0.45
0.29
0.29
0.31
0.07
0.07
0.07
MS
1.11
1.11
1.09
1.14
1.14
1.13
0.84
0.83
0.83
MT
0.97
0.74
0.74
0.96
0.72
0.72
0.97
0.46
0.49
NC
0.94
0.94
0.89
0.53
0.53
0.53
0.53
0.53
0.53
ND
2.03
2.02
1.71
1.51
1.51
1.43
0.62
0.62
0.59
NE
0.39
0.39
0.38
0.26
0.26
0.25
0.05
0.05
0.05
NH
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NJ
1.17
1.17
1.15
1.20
1.20
1.21
0.92
0.92
0.92
NM
0.46
0.46
0.46
0.45
0.46
0.46
0.57
0.57
0.57
NV
0.66
0.67
0.69
0.76
0.76
0.78
0.70
0.70
0.70
NY
1.07
1.06
1.08
1.00
1.00
1.00
0.68
0.68
0.68
OH
0.78
0.79
0.78
0.65
0.66
0.63
0.50
0.51
0.51
OK
0.70
0.70
0.67
0.70
0.70
0.67
0.12
0.12
0.12
OR
0.64
0.63
0.68
0.32
0.32
0.32
0.17
0.18
0.18
PA
0.98
0.98
0.97
0.97
0.97
0.97
0.84
0.84
0.84
RI
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
0.96
0.95
0.96
0.74
0.74
0.74
0.68
0.68
0.68
A-21
-------
Baseline
t. , r, „ More Stringent Regulatory
Proposed Regulatory Option ® "
State Tag
2028
2030
2035
2028
2030
2035
2028
2030
2035
SD
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TL
1.31
1.31
1.31
1.31
1.31
1.31
0.00
0.00
0.00
TN
1.17
1.17
1.17
0.50
0.50
0.49
0.41
0.41
0.41
TX
1.29
1.29
1.27
1.09
1.09
1.05
0.74
0.74
0.69
UT
1.20
1.20
1.22
1.26
1.24
1.26
1.23
1.26
1.27
VA
0.95
0.95
0.95
0.94
0.94
0.93
0.69
0.66
0.65
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
1.39
1.39
1.43
1.77
1.75
1.76
1.78
1.69
1.68
WI
0.66
0.66
0.66
0.59
0.59
0.58
0.43
0.43
0.43
WV
1.14
1.14
0.95
0.81
0.81
0.82
0.08
0.08
0.08
WY
1.24
1.24
0.89
1.41
1.41
0.99
1.56
1.55
1.11
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source
apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For primary PM2 5,
the following emissions change assignments were applied: DC -> MD, NH -> ME, RI -> CT, SD -> ND, VT ->
NY.
A.5 Air Quality Surface Results
The spatial fields of baseline AS-M03 and Annual Average PM2.5 in 2028, 2030 and
2035 are presented in Figure A-6 through Figure A-l 1. It is important to recognize that ozone is
a secondary pollutant, meaning that it is formed through chemical reactions of precursor
emissions in the atmosphere. As a result of the time necessary for precursors to mix in the
atmosphere and for these reactions to occur, ozone can either be highest at the location of the
precursor emissions or peak at some distance downwind of those emissions sources. The spatial
gradients of ozone depend on a multitude of factors including the spatial patterns of NOx and
VOC emissions and the meteorological conditions on a particular day. Thus, on any individual
day, high ozone concentrations may be found in narrow plumes downwind of specific point
sources, may appear as urban outflow with large concentrations downwind of urban source
locations or may have a more regional signal. However, in general, because the AS-M03 metric
is based on the average of concentrations over more than 180 days in the spring and summer, the
resulting spatial fields are rather smooth without sharp gradients, compared to what might be
expected when looking at the spatial patterns of MDA8 ozone concentrations on specific high
ozone episode days. PM2.5 is made up of both primary and secondary components. Secondary
PM2.5 species sulfate and nitrate often demonstrate regional signals without large local gradients
A-22
-------
while primary PM2.5 components often have heterogenous spatial patterns with larger gradients
near emissions sources.
Figure A-6 through Figure A-l 1 also present the model-predicted air quality changes
between the baseline and the two regulatory options in 2028, 2030 and 2035 for AS-M03 and
PM2.5. Air quality changes in these figures are calculated as the regulatory option minus the
baseline. The spatial patterns shown in the figures are a result of (1) the spatial distribution of
EGU sources that are predicted to have changes in emissions and (2) the physical or chemical
processing that the model simulates in the atmosphere. The spatial fields used to create these
maps serve as an input to the benefits analysis and the EJ analysis.
While total U.S. NOx emissions are predicted to decrease in both the proposed policy
scenario and the more stringent policy scenario for all years when compared to the baseline,
predicted NOx emissions changes are heterogeneous across the country with increases predicted
in some states. Figure A-6 and Figure A-7 show that the two policy options are predicted to
predominantly result in ozone decreases in 2028 and 2030 with the largest predicted ozone
decreases in the proposed policy option occuring due to decreased NOx emissions in West
Virginia and the largest predicted ozone decreases in the more stringent policy option occuring
due to decreased NOx emissions across multiple states in the Northern Plains and Midwest
regions. Figure A-8 shows that for 2035, increased NOx emissions that are predicted in both
policy options in Nevada and Utah would result in ozone increases in those states while
decreases in predicted NOx emissions would result in ozone decreases in other parts of the
country. For the proposed policy option, the 2035 NOx emissions decreases and resulting ozone
decreases are largest in Mississippi and Montana, while for the more stringent policy option, the
2035 NOx emissions decreases and resulting ozone decreases are predicted to occur over a large
number of states in the Northern Plains and the Eastern U.S.
Both secondary and primary PM2.5 contribute to the spatial patterns shown in Figure A-9
through Figure A-l 1. For the proposed policy option, the predicted PM2.5 decreases evident in
the Northwestern U.S. and Northern Plains regions are predominantly driven by predicted
primary PM2.5 emissions reductions in 2028 and 2030 and by a mix of predicted primary PM2.5
and SO2 emissions reductions in 2035. For the proposed policy option, SO2 emissions reductions
play an important role in the predicted ambient PM2.5 reductions in the Ohio Valley and Mid-
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Atlantic regions. For the more stringent policy option, the PM2.5 decreases evident in Montana
and North Dakota are primarily driven by predicted changes in primary PM2.5 emissions. PM2.5
decreases evident from the more stringent policy option in Wyoming are driven by a mix of
primaiy PM2.5 and SO2 emissions decreases and the PM2.5 changes in other areas of the country
are primary driven by predicted changes in SO2 emissions. In 2028 and 2030, SO2 emissions are
predicted to decrease when totaled across the U.S. but are predicted to increase in some locations
and decrease in others, leading to predictions of heterogeneous ambient PM2.5 changes.
Specifically, predicted increases in SO2 emissions in Texas and Georgia lead to predicted local
PM2.5 increases in 2028 and predicted increases in SO2 emissions in Missouri lead to predicted
local PM2.5 increases in 2030. Predicted 2028 SO2 decreases greater than 1,000 tpy in Florida,
Indiana, Michigan, Nebraska, Ohio, West Virginia, and Wyoming lead to predicted PM2.5
decreases in those locations. Predicted 2030 SO2 decreases greater than 1,000 tpy in Florida,
Kentucky, Nebraska, Ohio, and Wyoming lead to predicted PM2.5 decreases in those locations.
Predicted 2035 SO2 decreases greater than 1,000 tpy in Kentucky, Montana, and Wyoming lead
to predicted PM2.5 decreases in those locations.
Figure A-6 Maps of ASM-03 in 2028. Baseline ozone concentrations (ppb) shown in left
panel. Change In ozone in proposed policy option compared to baseline values (ppb) shown
in center panel. Change in ozone in more stringent policy option compared to baseline
values (ppb) shown in right panel.
A-24
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2030 Baseline
1
2030 Proposed Option
2030 More Stringent Option
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Figure A-7 Maps of ASM-03 in 2030. Baseline ozone concentrations (ppb) shown in left
panel. Change in ozone in proposed policy option compared to baseline values (ppb) shown
in center panel. Change in ozone in more stringent policy option compared to baseline
values (ppb) shown in right panel.
2035 Baseline
1
2035 Proposed Option
%
\ (
2035 More Stringent Option
%
-i-JI
Hn - 24371«(395.1H Mb * 70,326 M.
it (i*L 192), Mac *• 0,115 K (102.136)
1 n - 0 853 ot (141.1921 Wa< - 0127 at (102.136)
Figure A-8 Maps of ASM-03 in 2035. Baseline ozone concentrations (ppb) shown in left
panel. Change in ozone in proposed policy option compared to baseline values (ppb) shown
in center panel. Change in ozone in more stringent policy option compared to baseline
values (ppb) shown in right panel.
if
\ *
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.
Figure A-9 Maps of PM2.5 in 2028. Baseline PM2.5 concentrations (fig/in3) shown in left
panel. Change in PM2.5 in proposed policy option compared to baseline values (jug/m3)
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shown in center panel. Change in PMii in more stringent policy option compared to
baseline values (p,g/m3) shown in right panel.
Figure A-10 Maps of PM2.5 in 2030. Baseline PM2.5 concentrations (ug/1113) shown in left
panel. Change in PM2.5 in proposed policy option compared to baseline values (jtig/m3)
shown in center panel. Change in PMii in more stringent policy option compared to
baseline values (jig/ni3) shown in right panel.
Figure A-11 Maps of PM2.5 in 2035. Baseline PM2.5 concentrations (ug/1113) shown in left
panel. Change in PM2.5 in proposed policy option compared to baseline values (jig/m3)
shown in center panel. Change in PM2.5 in more stringent policy option compared to
baseline values (jig/1113) shown in right panel.
A.6 Uncertainties and Limitations of the Air Quality Methodology
One limitation of the scaling methodology for creating ozone and PM2.5 surfaces
associated with the baseline or regulatory control alternatives described above is that the
methodology treats air quality changes from the tagged sources as linear and additive. It
therefore does not account for nonlinear atmospheric chemistry and does not account for
A-26
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interactions between emissions of different pollutants and between emissions from different
tagged sources. The method applied in this analysis is consistent with how air quality estimations
have been made in several prior regulatory analyses (U.S. EPA, 2012, 2019, 2020a). We note
that air quality is calculated in the same manner for the baseline and for the regulatory control
alternatives, so any uncertainties associated with these assumptions is propagated through results
for both the baseline and the regulatory control alternatives in the same manner. In addition,
emissions changes between baseline and regulatory control alternatives are relatively small
compared to modeled 2026 emissions that form the basis of the source apportionment approach
described in this appendix. Previous studies have shown that air pollutant concentrations
generally respond linearly to small emissions changes of up to 30 percent Cohan (Cohan et al.,
2005; Cohan and Napelenok, 2011; Dunker et al., 2002; Koo et al., 2007; Napelenok et al., 2006;
Zavala et al., 2009). A second limitation is that the source apportionment contributions are
informed by the spatial and temporal distribution of the emissions from each source tag as they
occur in the 2026 modeled case. Thus, the contribution modeling results do not allow us to
consider the effects of any changes to spatial distribution of EGU emissions within a state
between the 2026 modeled case and the baseline and regulatory control alternatives analyzed in
this RIA. Finally, the 2026 CAMx-modeled concentrations themselves have some uncertainty.
While all models have some level of inherent uncertainty in their formulation and inputs, the
base-year 2016 model outputs have been evaluated against ambient measurements and have been
shown to adequately reproduce spatially and temporally varying concentrations (U.S. EPA,
2022a).
A.7 References
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Emissions: Source Apportionment and Sensitivity Analysis. Environmental Science &
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Dunker, A. M., Yarwood, G., Ortmann, J. P., & Wilson, G. M. (2002). The Decoupled Direct
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Zavala, M., Lei, W., Molina, M. J., & Molina, L. T. (2009). Modeled and observed ozone
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-23-002
Environmental Protection Health and Environmental Impacts Division April 2023
Agency Research Triangle Park, NC
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